There's a third option -- unicorns/pre-IPO (minimum series D, preferably E/F+) companies that I think provides a nice middle ground

ML Engineer

Sai Shreyas Bhavanasi

With how good AI is these days you rarely need to read a paper cover to cover. I would just plug it into claude/chatGPT and ask it questions and "chat" with the paper. that 20-30 mins of chatting should give you enough of a high level idea where if you want to you can then dive deeper

I would also look into talks/lectures if you find that interesting

I will say normally users prefer web to test things out because its easier. The friction of having to download an app is really really really high as opposed to clicking the link and being able to reach your target action in a few clicks.

Also, no one wants to download yet another app which can just be a website and then get tons of notifications, it then forces you to sign up and clogs your inbox.

Just need to make sure your app is actually designed well for mobile users. Most web apps are horrendous for for web use bc its treated as second class citizen.

In fact when designing web apps you should almost always design mobile first bc when you tell someone to check out the app they will most likely check it from a mobile phone even if they dont use it on mobile

> Because the mobile app is the only way to get users

That's not necessarily true. A user is a vague term and can mean anything. I prefer to count a user as someone who does a core action on your app/meaningfully interacts/contributes to your north star metric. For example on taro it might mean upvoting a post or replying or spending &gt; 30 seconds watching a course

The best way is to tie it to business value at the end of the day. This is often pretty hard, but did reducing the latency improve some KPI? For example:

[Amazon study: Every 100ms in Added Page Load Time Cost 1% in Revenue](https://www.conductor.com/academy/page-speed-resources/faq/amazon-page-speed-study/)

As someone who's extensively used all sorts of AI tools the power starts to fade after like 3-5k LOC projects. I still think we're extremely far away from AI coding to be able to autonomously carry out tasks at any codebase that's not like &lt;3 months of work. Let alone any large corp with millions of lines of code

One caveat to remember is that doubling headcount is not *always* a good thing. Until a company reaches PMF you want a super lean company. Often times startups will rapidly add headcount and then also start trimming once they realize the product isnt gaining traction. Often times startups run as lean as they can until series B atleast 

Given that you're already in big tech I presume youre only targeting big tech. for most big tech MLE roles it's really just a regular SWE loop + 1-2 rounds of ML focus. So you don't need anything overkill for clearing ML interviews at a big tech level. 

I suggest just giving it a shot anyways instead of first trying to get focused experience/doing something as drastic as transitioning teams.

With the caveat that I'm not sure how easy it would be to do this at the senior level. From what I'm seeing getting senior SWE roles at big tech in itself is hard right now in this market so landing SWE MLE without prior core MLE experience might be more challenging. Though definitely possible at the mid-level

I recommend these resources:

For ML depth. <https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969> (I know the book is about system design but I think it does cover a lot of fundamentals needed for depth)

Here is a cliff notes for the above: <https://github.com/serodriguez68/designing-ml-systems-summary>

For ML system design I suggest byte byte go's ML system design. I've gone through this and it was fantastic. <https://bytebytego.com/courses/machine-learning-system-design-interview/visual-search-system>

I'm most interested in #8: Cracking the Code of Manager-Engineer Communication: Common Mistakes and Solutions

Currently there is no course on Taro that deals with this specific aspect. There is a communication course (havent gone through that one yet!) but your manager is the most important person in your career. Being able to develop the skills to communicate better with manager is something I am interested in

Especially as someone more junior, it's hard to understand the manager's "perspective" because most of what juniors do is focus on coding not managing

In my opinion until you have an offer in hand I wouldn't sabotage an interview intentionally.

While yes, you could ask the recruiter to give you a fast track option given that youre in the process with other companies, in this market you need to be perfect to land a job.

This is an extremely competitive market and I wouldn't risk it. Worst case this would be good for practice and if you can crack an in person interview then it'll really prep for you other online interviews. A lot of top companies will have in person interviews so this would be good practice

I'm not at adobe but from what I've seen from Adobe's job postings there's tons of scope for ML at adobe. Especially with genAI they're really focused on productionizing some genAI stuff for image editing (the firefly team)

I love this question! I haven't worked directly with running code gen agents, but I've done a ton of prompt engineering for other stuff like parsing unstructured data and generating insights

Here is a guide I suggest: <https://www.promptingguide.ai/>

I used this to get started.

Big things:

- Make sure to clearly give the bot all the info needed. This means clearly specifying here is the input API and here is the target API. Make sure to clearly specify to the bot to NOT make up any APIs -- like literally tell it to not hallucinate and it should only use the specified APIs. Obviously do some string checking to validate the response.
- Use structured outputs. I suggest using GPT4o-mini's structured outputs mode. It works fantastic in ensuring that you get the right inputs/outputs/structure every single time. GPT4o-mini is also really cheap :)

Small things:

- Give examples if possible. Examples help a lot
- Use XML tags to give a template.
- Lots of trial and error to be honest, make sure to have a way to systematically evaluate the "accuracy". This doesn't need to be automated but just a script to try a prompt and save the outputs to a file so that you can log and compare different prompts

Other details:

- You can do a surprising amount of stuff with smaller 7-B param models. As a rule of thumb first validate that it works with large models, then scale down as much as possible
- Complex reasoning/code gen: use large models
- semi-complex tasks: understanding complex unstructured data and making decisions (e.g. classifying food items so you give input: gummies and expect output vegan/vegetarian/non veg) use a medium model
- Paraphrasing/summarizing/text extraction: 7B param model

If your company allows it, you can look into using cursor for running code on your repo

Founding ML Engineer @ Lancey (YC S22)

Yes, but it typically depends on what are you trying to do with ML. I dont suggest learning ML from first principles typically because 1) there's so many verticals within ML its impractical and inefficient and 2) most of the time you actually dont need to know ML if you're working with LLMs.

For LLMs in specific I suggest just building stuff with them. You really dont need to know any classic ML to fine tune LLMs. Most of the times it will depend on the model and the platform you're using and following the docs. Think of a simple project and learn as you go to build that project out.

But if you do want to learn classic ML Engineering I suggest this crash course: <https://github.com/serodriguez68/designing-ml-systems-summary/tree/main>

It has everything you need to know about real world ML without the ML theory.

Should you want to learn classic ML theory I suggest this book: <https://d2l.ai/>

ML roles vary widely between companies. Data Engineer can be anything from Database Admin/"SQL monkey" to creating pipelines using spark to writing scala/java code working on the data infrastructure.

The classic quintessential bay area data engineer role is one where you're working on large scale data pipelines using (py)spark.

Im curious what the size of the company is. This doesnt sound like a dedicated "Data Engineering" role to me and sounds more like an ML Engineering/ backend engineer job especially given there is no SQL based work.

And most of the work of an ML Engineer is cleaning/parsing the data. This is more true for smaller companies where you dont need a dedicated engineer for handling the data.

Honestly I wouldnt take this if youre looking to learn ML. LLM stuff is really just software and people treat LLMs like a blackbox.

I will also say frontend -&gt; MLE is a huge difference in verticals and quite honestly there is not much overlap other than meta skills you get with writing software in general

For side projects my goto is to ask friends (or people in Taro) what the status quo is and do that. Don't overthink it. Use perplexity to speed up research. Give it your details and ask it to make a suggestion.

I feel like frameworks follow the 90/10 rule. 90% of the features will be similar and only 10% differ. For side projects you'll almost certainly not hit these niche requirements

Also in terms of database, schemas are supposed to be evolving. Most libraries will have some migration handler to handle changing schemas so you should definitely not spend more than a few mins worrying about schemas.

In general most learning happens when you can no longer do what you did before because of a new constraint. When you naturally hit these constrains your learning 10x'es

The skills location bit is one of the things I think doesn't really matter. Recruiters know to skim/skip/parse out as needed so whatever you feel fits best. I tend to default to bottom but it honestly doesnt make a difference imo

On my agenda

App side:

- Adding filters (cuisine, entree/side)
- Pagination
- Moving to custom backend
- Improve data quality (shows a lot of sauces/sides/irrelevant or low quality recipes)

Question:

- Should I focus on
  1. adding features (filters/pagination) first and then worrying about good data/recipes or
  2. get a good list of recipes/data quality and then worry about UX/UI features?

I'm leaning towards 2. Get minimal but high quality set of recipes -&gt; market it on reddit -&gt; then improve features. But I do see the appeal in grabbing the low hanging fruit and make the app functional and then worry about data quality.

Data quality is surprisingly non trivial. The API I'm using has a lot of limitations so I'll need to get something custom working which will take a bit of time. Also determining if a recipe is vegan or not is surprisingly tricky. For e.g. if you have a recipe use wheat thins as an ingredient how do u know if wheat thins is vegan?

Side Note: frontend is SO hard. really feels humbling to be brand new at something after spending so long building depth in backend/ML

My goto advice when it comes to switching to ML is to find SWE roles in ML teams. There is a surprising amount of SWE work needed to be done within ML teams just for the deployment of a model. And no I'm not talking about MLOps.

For e.g. you need someone to get data from the user to your server, then you need to process the inputs for the ML model, it needs to be deployed and scaled, then after inferencing you need to surface back the predictions to the user. 

You also need to do testing for all this. When you run it at scale even this inferencing can be very complex to handle such large volume of data and there are also constraints with needing real time or batching predictions. You get the idea

I don't suggest pre-learning tons of ML and Data Science. I go into depth on this here: <https://www.jointaro.com/question/48WePYRfWEXgc5itTYUY/new-grad-backend-greater-mle/>

For a student my biggest tip is company and team matters more than what you do in the internship. Most internships are too short to do any real quality work. **optimize for company prestige, not role.**

For MLE its more optimal to find a SWE internship and then transition later on when u get full time offer. Most MLE roles expect a solid SWE foundation so not having ML experience wont hurt you. Just make sure to have 1-2 projects and you should be good

If youre happy with the interviews youre getting then work on leetcode. If youre not happy then projects, tho my caveat is that in this market getting big tech is extremely hard

Dont spend time with system design for internships. All that matters is DSA and cracking OAs

tbh its hard to say but my general thesis is that there are a lot lot of good companies out there so dont worry about messing up a single opportunity.

Apply to anything as soon as it opens up. There's 3 scenarios

1. You apply ASAP, get an interview ASAP because they close out positions soon (no loss because if you waited you wouldnt even get this intv)
2. You apply ASAP but they wait till August to start interviewing (ideal)
3. You wait till August but by then theyve closed out the positions and the posting is just there in case someone bails out last minute (best case, but less likely)

Youd rather fail an interview than not take an interview. If you applied early and got an interview early you have very little to lose because they likely would close the position before you're interview ready

Every company does things differently but generally for internships/new grad most companies tend to start interviews around Oct ish to roll out offers before thanksgiving. But a lot of companies tend to hire on a rolling basis and close out soon. Google's 2025 internship I think already closed

In general I think there's so much variance in how things play out it's hard to predict how recruiting goes. Even with the same company when they hire and stuff is just so random.

The key to getting interviews for newgrad/internships is to get insanely good at hackerrank stuff. If you can crack those OAs you'll get tons of interviews

At the end of the day **volume conquers variance** so just keep applying and take as many stabs at the goal as possible

You should start applying **now**. This cycle is moving fast and Sept is definitely too late. Just keep following along that list and keep applying as they open up. You'll never truly feel ready but you just gotta roll with the punches. I find that the pressure of having an interview pushes me to prep hard. People use lists such as the simplify list: <https://github.com/SimplifyJobs/New-Grad-Positions?tab=readme-ov-file>

Typically people just list the company and put the dates and add 1 bullet just talking about the team/role and expected work if youre applying for new grad and have an incoming internship

Another litmus test is if you have any external stakeholders/obligations/contracts. For side projects you can always just stop and its ok. But if you cant just stop or have a contractual agreement for providing a service I would be ok calling it work experience

One suggestion -- If you have external monitors I presume you can also use an external keyboard which would eliminate having to get used to mac shortcuts

I suggest looking into software solutions

For an internship I had to use Microsoft Intune which applies a software partition to download work apps and use work email. I had an option on my phone to silence the notifications from that work profile if I wanted to.

<https://play.google.com/store/apps/details?id=com.microsoft.windowsintune.companyportal&hl=en_US>

Another option is just adding in workflow/routines on your phone to mute notifications from apps after a certain hour using a quiet mode/DND feature

Your salary is *generally* tied to the cost of living. So typically high cost of living should not be an issue in my opinion. The obvious caveat is that few companies do have a set salary regardless of location for fully remote roles or if you're self employed

Regardless I think SF is the best place to start your career if you have the opportunity to do so

Rahuls advice is great. Generally I try to approach disagreements as a "Hey I dont think Im understanding this, can you help me understand it better? I have done XYZ so far but I think I'm missing the gap on this"

In doing so 2 options come up

1. I misunderstood whats being asked and its actually not bad task
2. The manager realizes that they were mistaken

And this comes off as less conflictive because you're not disagreeing, youre just asking for help. To put it bluntly dont frame it as a "youre wrong, here's why" frame it as "I see what youre saying, I think its this but can you help me understand where I'm wrong"

Use wifi calling, that's what I do!

Also as a rule of thumb always call a friend via the interview method before any interview to verify call quality. I do this if I have a teams interview and ive never used it before and so on

<https://support.apple.com/en-us/108066>

<https://support.google.com/phoneapp/answer/2811843?hl=en>

First, a 5% pass rate is actually **really really good** which tells me you have a really strong profile so congrats on that. Given that I think your focus should then be on converting interviews to offers.

Most students (myself included) see a 1-2% pass rate and its even worse in this market and typically people apply to \~300-500 jobs per cycle

I would then try and do it this way

9-6 internship, 30-60 mins/day leetcode. Weekends research/leetcode/project

While I know the video youre talking about with LC 20hrs/week, I personally think with leetcode you go really far with even just a bit every day. The good thing about leetcode is that you dont need long focus periods (like research) as you can tackle a problem quickly and move on

Even if you do 1 problem a day by the time interviews start rolling (which tends to be before thanksgiving generally) youll have done 90-100 problems which should be pretty solid

I think the limiting factor is quality not quantity -- when taking on so many different tasks I find that my quality starts taking a hit. So I suggest prioritizing by stack ranking whats the most important and keeping focus blocks (8-9 hours a day) and doing whatever you can in that block instead of throwing more hours at the problem

Are you getting interviews? If yes then do leetcode instead of project. No? focus on projects

But for the most part I suggest prioritizing getting that return offer, then the research perhaps on the weekend. If you have spare time do some leetcode

I find that when I spread myself too thin I dont get anything done and instead just prioritizing and doing fewer things properly gives me better results in the long run

My #1 focus would be making sure you get that return offer. Return offers are not a guarantee in this market especially at startups. And its not your fault. You can do everything right and not get RO. Make sure to apply to new grad postings, have your resume in check and be somewhat interview ready. No need to be full on interview ready but you should be able to ramp up in 2-3 weeks if needed.

During the internship I would focus primarily on getting that RO and networking. If you get an RO then I would relax a bit, take it easy and build some fun projects. 

Should you not get an RO I would focus on applying to new grad jobs and practicing DSA/Leetcode

> 1\. How much ChatGPT should I use when building my projects? I feel like if I use it too much, I won't develop coding skills as much and also non-technical skills like reading documentation. Opinions?

It depends, are you building projects to learn a framework/language or to get users? If its the first type then dont use it for everything i.e. dont build a full crud app with 1 chatGPT query if youre trying to learn rust or something. But if youre trying to build cool stuff and not worried about learning then use chatGPT

Agree with Sameer, get your resume in check, but roughly estimating knowing the stats is super important. How many apps have you sent? how many callbacks? what portals have worked? what types of companies are reaching out? no need for exact numbers but some rough estimate is helpful

Adding to this, ML has a tonnnn of breadth. the modeling aspect (which is what gets all the hype) is like 5% of the employees on an ML team and 95% is everything needed to get a model to production and im not even talking about SWE teams using the output of ML models in their work.

A lot of times the modeling aspect is gatekept to people with PhDs because at the scale of big tech where things are already hyper optimized you need a team of PhDs to make even the smallest improvement to the models

Hey OP, see my answer on this thread here: <https://www.jointaro.com/question/48WePYRfWEXgc5itTYUY/new-grad-backend-greater-mle/>

The main insight here is find SWE tasks on ML teams. I really dont suggest getting a masters or taking specialized training for this. It's far easier to switch internally than to make a double change (change companies + role in 1 go) in this market

Happy to chat about it on slack if you have more questions. 

Can you share more details about stats? what is the percent success for each?

<https://sai-translation.streamlit.app/>

My app where I wrote a custom algorithm for translating between languages. The project was part of a larger project to compare different algorithms/papers/models for translating between languages that are not widely spoken (e.g. Irish)

> 1\. Should I put months for each year for all my experiences or is it okay to just put the years? For example if this hides a 6 month gap? I've heard very conflicting opinions on this!

I would not remove months. That's too shady and you're better off with a 6 month gap. I feel like HMs would feel like you're trying to hide something and immediately discard

> 2\. If I'm in a city in California that's not super close to San Francisco, is it okay to just put California and leave my city out so I'm not auto rejected for positions that are in San Francisco, since I'm open to relocation?

Yes that's fine. As long as you tell them that you're open to jobs in that location. In my resume since I live in a random non tech city in the midwest I said "Open to any US location" and got a bunch of interviews as a new grad. Of course it doesnt mean that I got interviews because of it, but i did put that in my resume and it didnt hurt me. YMMV

> Also should I say somewhere on resume that I'm open to relocation?

Yeah you can probably put both, city and open to relocation

> 3\. Pretty sure the answer to this is no but figured I'd ask! Is it worth going from 1 to 2 pages to list a few side projects that are over a few years old now and didn't really get a lot of users?

I would not recommend going over 1 page. Only put the most impressive side projects. 

The bar for side projects is higher than work experience so be very selective with side projects. Side projects are seen as less valuable than work experience. So for a side project to be worth of the real estate on the resume it has to be very strong. In other words the work experience in work is more valuable than work done building a side project unless the side project is very impressive (has 1000s of users)

Also I'm fairly certain most recruiters/HMs wont read the actual bullets of the projects unless its very impressive i.e. has users

Rice cooker is simply the best cooking appliance one can purchase. For my summer internship across the country I took my rice cooker in the flight in the baggage lol

Similar to others here I try to keep my meals where I cook once and it lasts a few batches. I try to do the following:

Breakfast: oats w/ strawberries + nutella/unsweetened almond butter

Lunch: sandwiches (good bread, deli meat, sauce, veggies)

Dinner: Usually 

- Dal (lentil stew)
- a dense bean salad - cannelini beans, onion, bell peppers, cucumbers, cherry tomato, deli meat/rotissierie chicken + dressing (olive oil+lemon juice) + chickpea pasta/quinoa
- veggie south indian curry

Snacks: Youghurt cups/plain greek yoghurt/granola or protein bars/fruits

My 2 cents: as a new grad the "safer" option is one with more opportunity. and imo if you feel indifferent go with the safer option. If you're a backend engineer (I think, please correct if im wrong) its much easier to go to android compared to the other way (android to backend engineer).

I say that because android is effectively a specialization of SWE -- like its a specialization of frontend SWE. Typically the bar for entry in specialized roles is going to be much higher and is generally something like (3yrs of SWE out of which 1-2 is mobile dev). You can always build mobile projects to get that experience. It's obviously possible to get back to backend from mobile but it's in my opinion tougher and as a SWE it's not ideal to have to switch around tech stacks. and if you've got 3 YoE of android a HM will favor someone who has 3 yrs of backend exp w/ springboot over someone with mobile experience.

As a new grad in this market, staying where there are more jobs will be the safer bet. I'm sure the pool of jobs for mobile dev is much smaller than the pool of jobs for backend which is going to limit your opportunities/teams to strictly companies that have a mobile platform. Stuff like B2B SaaS will not be an option.

*This is based on my experience specializing in ML, I've never done android so take this with a grain of salt 😅*

Alex, how do you measure impact of **your** work when working in a large FAANG company where any impact is almost always a result of collaborating with others? If you optimized one part and then your coworkers refactored/advised you and your team lead helped you debug how do you isolate it? This is also related to resume/interviews when taking credit. 

Crunchbase is good, but most of it is paywalled and the pricing is not worth it for individuals. Most companies like to show off when they raise so you can generally find that information fairly easily. Companies are also generally willing to share info about funding and stuff when you ask during interviews

Always apply, but when interviewing ask these questions

- How much funding do they have
- How many engineers do they plan to onboard?
- Burn rate/runway (after onboarding all engineers)
- Investors (YC is a great sign, look up popular investors)
- Talent density - If the founders and engineers all come from top companies then generally they are better funded

Also get a sense of their engineers who left the company. Did they do layoffs? Are they pivoting? pivots are generally not a good sign

1 misleading indicator of health - Are they hiring aggressively?

- A lot of companies want to run lean so fewer engineers is not always a bad thing
- Conversely a lot of engineers might be a bad sign (overhiring)
- Generally companies should be pretty lean until PMF which tends to be series A ish

Pivots - I remember a sam altman video where he said some founders realize this current thing is not working and theyre running out of money, we need to pivot and they force an idea that more often than not fails

Try to understand the markets of their business. Does it seem profitable/revenue generating? Generally B2B is better (easier to monetize). User count for a B2C can be very very misleading as a lot of companies struggle to convert from free to paid users

Just happened to stumble on this. Would love to hear an update! @Alex

3 uses that jump to me:

- A GPT4 copilot that can watch your screen and help you pair program/debug
- Real time mechanic to help you correct your car or something
- Real time fitness coach to help you correct your form while training

<https://www.youtube.com/watch?v=wfAYBdaGVxs>\
Here they show a real time interview coach 🤯. Would be an amazing tool for system design and DSA prep

My criteria for picking side projects:

- Should be easily demo-able/show proof
  - Web apps are great because you can drop a link and interviewers can check it out. I've done in this interviews a couple times where I drop the link and they check it out while I'm explaining it
  - Android/iOS apps are great because people can check the playstore link and see it has X downloads
  - Something that is hard to demo/not publicly accessible like a CLI tool or software that runs private is generally less preferrable

When it comes to side projects dont over think it. Software can always be made more and more complex with additional features. Pick something you can hack together in 2-3 hours and keep iterating over months. I dont think any software is ever "done".

Even something as simple as a login page can be made enormously complex (string validation, SSO, login with google/apple/github, reset password, ensure new password is not the same as old, ensure it shows properly on windows/android/desktop, dark mode/night mode, passwords should match, username should be unique)

I wouldn't worry about picking something too easy or too hard. Just figure it out as you go! use chatGPT or google to learn what you need as you need.

If you're feeling stuck a simple idea is pick an app you use and clone it and give it out for free-- gym tracker, nutrition tracker, productivity apps like notes, chrome extension to track submitted job apps -- and then make it free

<https://www.jointaro.com/lesson/AI4zi759PSGn6RwIFT6f/masterclass-how-to-come-up-with-100k-users-app-ideas-you-can-build-for-free/>

1. In my opinion it is pretty tough to switch roles and new jobs at the same time. It might be better to look for SRE roles and switch internally. This job market is tough for entry level. You'll be competing with other SWEs. Companies are already taking a risk hiring a new candidate. It doubles the risk to hire a new candidate with different skill sets. Also when considering switching roles and levels, the best thing to do is just apply! You'll find out whether or not your skills are enough. Use simplify extension and spend 3-5 hours next week sending out 150-200 apps and you'll be able to gauge.
2. Yes I would focus on building projects. In my opinion I would focus on having 2 different projects -- build 1 project that's directly aimed at optimizing for users/impact. Another project that's aimed at showing you have relevant skills. The reality is that outside of big tech (which is not hiring jrs right now) F500s and startups want to see you have experience with the tools they use. So if you built a ton of projects with Python but they use springboot/go or something then they are less likely to hire you. Ideally you're able to do both with 1 project but sometimes that can be hard
3. Internships are super rarely given if you're not a student so that might not work. There are some apprentice programs so you can try that but they are generally super competitive
4. I don't think it's in your advantage to reveal a weakness like that especially with no real solution. Rahul often says you should be very strategic with what you reveal and don't reveal any information that can hurt you if you dont have to. Of course dont lie, but if it's not helping you then dont reveal it. 
5. I'm sorry to hear about the depression 😞. From what I'm hearing I would look into working with a therapist to address this and diagnose the underlying issue. If your mental health is affecting your life so much then to me it seems like something therapy would help a lot with

I found the following helpful when I was learning a new codebase: 

\
<https://www.jointaro.com/lesson/DX27hiXPJ4G7Czk2mwLL/how-to-learn-a-new-codebase-fast/>

<https://www.jointaro.com/lesson/u9ldIvrTvV1wfWExccDR/masterclass-how-to-learn-quickly-in-tech/>

<https://www.jointaro.com/question/F2rJZgdUBxdGZilFTR6k/how-can-i-learn-a-large-codebase-fast-as-an-intern/>

Use git blame to figure out the areas of high activity

Try to break the code intentionally and see what happens

There's also the new onboarding course from Rahul. I haven't gone through it fully but it might be helpful!

I would add that in terms of networking events, it's not just events that directly help you get a job that's useful, but even showing up to clubs/fairs is networking. I'm sure there are tons of guest speakers coming to GTech fairs and clubs. Even after you graduate if you know a ton of people then when you need a referral you'll be able to reach out to people you know from clubs

My biggest college regret is trying too hard to get good grades instead of job searching and networking

Sometimes showing up is all you need to do. I know people who got internships because they went to a club and some person who's working in an early stage startup was giving a talk. You never know who can help you get a job. Good luck!

For CS/SWE internships, getting really good at Online Assessments (OA) is key. From the looks of it, you already have a strong profile. Since you already have enough projects/experience getting good at leetcode is a good next step.

Most internships send out an OA, and if you do well there's a good chance you'll get an interview. I would focus on grinding leetcode and cracking those OAs

I would also focus on networking. Attend career fairs, clubs, coffee-chats, any and every networking event you can find. You can try cold DMing alumni and getting coffee chats and then asking for a referral

I havent used Spark, but I used Dask which is a python first version of PySpark that also does distributed data processing. Below are my insights for debugging in dask but similar stuff should apply to spark

### Finding Bottlenecks

I found using the dask [dashboard ](https://docs.dask.org/en/latest/dashboard.html)really important (see [spark dashboard](https://docs.databricks.com/en/optimizations/spark-ui-guide/index.html))

- I look for memory issues - is there large spills? this is bad
- Are there too many tiny processes? this means that the spark is being really inefficient with too many small jobs - try increasing the partition size
- Is there too much data transfer? shuffles and joins are terrible so I avoid it as much as possible
- How well utilized are all the nodes? If a lot of nodes are sitting idle then that is bad
- Is the scheduler overwhelmed? this is bad

### Improving Performance

To improve performance without running large jobs I try to benchmark performance on small(ish) datasets and see how each change affects performance. Most spark/dask jobs are groupby + apply.

Spark basically parallelizes multiple tiny jobs. So if you can isolate and benchmark the tiny job to be as efficient as possible then this will carry over to the large jobs as well.

So I test the apply code on a small dataset and test how different changes to my apply function affects performance.

**In my experience 95% of the issues are from partition sizes**. If you're struggling with performance changing partition size is best first approach. Ideally start with 100MB partitions. Ensure that the partitions are not getting bloated throughout your code if you're adding/removing columns. If an individual partition/apply function takes a while, then reduce partition sizes.

Honestly the biggest performance gains came from reframing the problem/rethinking the approach to the actual problem you are solving. Talking with your coworkers and getting outside perspective helps a lot

Sometimes I get too stuck trying to optimize code and looking from an outside perspective you might realize you can trade off a bit of accuracy of the features for a huge performance gain.

For e.g. i remember this story of how instead of trying to make elevators faster they just put mirrors and people were busy looking at the mirrors so they felt the elevators are faster

Jonathan's answer is fantastic. This video was super helpful for me when I onboarded during internships: <https://www.jointaro.com/lesson/ukpEyEsSONZA0n6NFdAP/ask-questions-well/>

Mastering this skill of asking high quality questions will take you far

Hey OP, are you able to share more on how the ML is being applied? In most cases you don't need to spend too much time understanding the low level math for ML unless you're dealing with customizing models.

I would focus on understanding

1. How does this model work -- the intuition behind it
2. What are the assumptions this model makes for e.g. some models assume the data is normally distributed
3. What are the pros of using this model
4. What are the cons of using this model

If you understand this you should be good for most cases.

And if you do need to actually understand how the model works in depth I suggest only learning the parts you need to as you study

E.g. you are learning about Convolutional Neural Networks - CNNs

- Understand how CNNs work intuitively
- Then you want to know what a convolution is so you dive deep into the math/code
- Then you realize you need to understand what a dot product is, so then look at that
- Keep repeating till you understand everything, almost like a DFS tree search

Other things that help:

- Look at minimal code replicas of models. Every model you want will have some version of minimal version of it ([even LLMs](https://www.youtube.com/watch?v=kCc8FmEb1nY))
- Grab the code and walk through what's happening. For CNN you might look at how the image is before and after a convolution is applied. Step through the code and understand what is happening.
- Start small, work with really small data that you can reproduce by hand. For CNNs this might be an "image" that's 5x5 0/1 and you can do the math on paper on what each step should look like

Feel free to DM me on Slack (@Sai B) if you want to chat more

Agree with others here. Think of ChatGPT as a glorified stackoverflow (it's basically a compressed internet). It's important to understand why you're doing the things you're doing and you'll be fine

Ooo that's awsome. I've been sleeping on it. Thanks so much for sharing!!!

Would you be open to sharing how many callbacks you got from it? I've never heard back from easy apply

Honestly this is super relatable. As Rahul said, the rejection rate in recruiting screening is super high. My guess is that it happens bc the HM is looking for a very specific candidate with experience in X,Y,Z tech and maybe you just dont have it. There's thousands of candidates and the HM has no incentive to pick a candidate who isnt already using their tech stack. I am guessing the recruiter probably doesnt have a deep understanding of what specific skills/tech stack the HM wants and how transferrable some of the skills are

Hi OP, when you say dig deeper into ML, what exactly do you mean? are you looking for more R&D role? It seems like you're currently working on platform.

I'm sure at Meta the core ML is reserved for PhDs. But I've heard of MLEs who do more engineering related work of ML can be done with SWE experience. Open AI themselves said that they value SWE expertise more than ML expertise because its easier to teach someone to do ML than write good code.

Generally I think it's better to try and transition and learn on the fly rather than get another degree especially for ML. ML roles are either core ML which at big tech you'll need PhDs because they're specialized roles or they are supporting ML which is basically a different flavor of SWE.

The best option imo is to first try to break into ML by applying or trying an internal transition. Soon you'll realize what you're missing for your career transition. Best case scenario you dont need a masters for what you want.

I'm a bit confused. You graduate this summer and are also interning this summer?

Not mentoring, but I hold regular coffee-chats where you can meet other engineers. Taro engineers are amazing and in the past sessions, people have gotten referrals, career advice and more. I suggest joining! \
The next one is tomorrow (Apr 23) at 5PT: <https://www.jointaro.com/event/taro-virtual-coffee-chat/>

Are you currently interning at the company you want to return full time to? I suggest discussing with your manager about a return offer

The easiest thing you can do is not update your LinkedIn till you get a confirmation. Just say whatever you need to on your resume

Hey OP, here is a blueprint for a project.

It's great you've listed some interests of yours. I suggest going to Kaggle and finding a dataset you're interested in. The bigger the better (think 50/100+ GB) and if it updates everyday even better.

1. run some prefect/airflow or orchestration job to fetch data from some source (reddit/twitter/kaggle...) put it in s3
2. read from s3 and write a [sagemaker processing job](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#build-and-manage-steps-types) to train a model
3. deploy on sagemaker inference endpoint
4. wrap with a flask app
5. Run continuous training and monitoring. As new data comes in how does your model performance change? Are you able to easily retrain a model after time passes?

Always happy to chat over Slack DMs (@Sai B) or LinkedIn if you have more questions

Hi OP, can you share a bit about your background in database and what exactly you're looking to learn? Are you referring to learning more about querying data from DBs aka being a SQL expert, or designing/architecting DB schemas for your team? or maybe connecting/creating data pipelines from the DB to your software?

In my opinion, I suggest to ramp up how many applications you send. From what I've seen you should have a different resume for each genre of job so one or product focused, one for infra focused, or backend/frontend, and so on depending on your skillset. Once you have this when you apply just send in the resume tailored to the job app

I would target minimum 100 applications a week. I use [simplify.jobs](http://simplify.jobs) and I can do 100 in about 2-3 hours. Dont bother sending in cover letters. Getting interviews is a numbers game so mass applying is the way to go. 

Apply to any job you meet 50% of the requirements of. Good luck!

Definitely 50 is too much. Also find out if youre going to need to slot out time for a Q and A as well. I benchmark 1 minute per slide usually. 

For the presentation

- **Remember to talk about the problem you are solving**. The audience (assuming its seniors/managers/tech leads) wont know your problem so make sure to be specific about the problem you're solving
- **Talk about solution in a high level**. These folks likely see a bunch of these and they get fatigued through it. No one can really follow your actual solution, so make sure to keep it high level
  - The level of technical depth should be as if you're presenting to a recruiter
- **Thank the people properly** who helped you out in your internship
- **Emphasize growth and what you learned**. Interns are almost always a net negative for companies. They also know you've likely never had a real SWE job before so growth and learning speed is very important. Make sure to highlight soft/hard skills you learned during your internship
- Even though it seems like your internship was less than ideal, please try to not project it or talk negatively during this time. Try to keep things positive
- When you present try to stay in a positive mood and be a bit energetic. It realy reflects positively

I think generally most companies are flexible with start dates. At my internship, the company was willing to move dates to accommodate my college's international student CPT requirements. 

I would reach out to the recruiter/HR and just ask if you can move the start date by two weeks so that you can finish your conference and start the internship.

May 1st is also really unusual start date for an internship so its not going to be out of the norm to ask to move the dates. Normally its may 2nd week if its 12 week or june first week if its 10 week internship 

AWS is very good at refunding/refuting bills if you accidentally rack it up. I was working on a personal project and somehow launched some ML canvas that runs and charges until you shut it down. I swore I closed it but I kept seeing my bill increase daily. In the end it was $300.

I reached out to AWS support and turns out it was open in a different region. I spent 2 hours working with support. You have to manually toggle to that region and only then it shows up . I was like how is it charging me I literally dont see it running. Luckily AWS helped me through it and refunded everything. Normally for small charges as long as you act promptly they will refund it.

This is mainly for services left running though. I'm not sure how AWS works if you rack up a large bill because of an attack.

It's quite important to get familiar with cloud so my personal suggestion is to go through the flow and get everything running for the learning experience and shut it down. Then deploy it elsewhere like [railway.app](http://railway.app) or heroku. I like railway a lot

As Daniel said, you can also set billing alerts and also alerts for unusual activity.

To be honest what I've learned is that there's so so so many tools and technologies and every company does things a different way -- so it's just best to learn as you need to. I'm not sure how much background you already have in ML. If you've already done some projects then I would not worry too much about keeping skills sharp

### Project Framework

If you havent had any project experience, I would do an end to end AWS project

Pick a dataset of your choice (ideally that's big data - over 10-20ish GB, something that wont fit in RAM) 

Put into S3

Write some ETL scripts to create features

Train a model on it like XGBoost and use some logging tool like wandb

Deploy it using sagemaker endpoint and docker and create a simple flask API/website

setup some continuous testing/monitoring to monitor the quality of predictions in the cloud

Bonus points:

- If someone uses your model and you have users
- You are able to support retraining

I would avoid deep learning and stick to tabular data. Most companies apply ML to tabular data

### Resources

[Summary of Designing Machine Learning Systems](https://github.com/serodriguez68/designing-ml-systems-summary)

A fantastic summary of Chip Huyen's book - Designing ML systems -- The gold standard of MLE

<https://www.ml.school/>

An ML course that focuses on the engineering side of ML (deploying ML, feature engineering, how to tackle ML problems). It's a fantastic course that is super industry oriented and teaches how to use sagemaker and other fundamental ML concepts. I've taken it and it's brilliant and is the best i've seen. Though if you've gotten a master's focusing on MLE it might not be super useful. Check the syllabus and see if it applies for you. If you havent done any cloud work with sagemaker this might be helpful

Congrats on getting your masters! From my understanding, ML infra is generally a SWE for the backend SWE that happens to be for ML but the work is basically SWE.

In terms of skills for MLE, the core things that matter and keep you relevant are

- Are you able to build data pipelines to feed the model the data
- Are you able to train a model that works well
- Are you able to take a model and deploy to production
- Are you able to evaluate, benchmark, and run experiments with the model
- Are you able to monitor the model when it is in production?

You can be an MLE without ever training a model. In my opinion MLE is basically a full stack data scientist

Based on what you said, it seems like you're basically doing SWE work. I would look into transitioning into a different team that does one of what I mentioned above. Is that something possible for you?

My guess is that if your company is building infra, then they should also have a team responsible for training/testing/deploying (unless the company is a pure infra team)

[simplify.jobs](http://simplify.jobs) was what I used to use all the time looking for new grad roles

- When you hit apply it one click auto completes using the chrome extension allowing me to get through 100 apps in 2 hours
- You can apply their filters to filter roles based on industry and also their level filter is by far the best I've used

On LinkedIn you need to use filters for best results. Here's mine:

```
("machine learning" OR "ML" )(NOT "senior" NOT "sr" NOT "manager" NOT "lead" NOT "principal")
```

I enter a long boolean search directly on google search. I also set a time filter on google search on the top to filter down to posted in last 24 hours

```
site:lever.co ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

```
site:boards.greenhouse.io ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

Have to mention [Microservices video](https://www.youtube.com/watch?v=y8OnoxKotPQ)\
This is also what I hear from Taro team when I say "why cant we just filter on all users" 😂

I think having some mental separation between work and not work can help. So this might mean going for a workout/walk or a commute and an unwind routine

My main issue has been having the energy. Work is so draining so I find that working out frequently just generally increased my overall energy levels

I would treat this like any other ML project. I would build an end to end app where you

1. run some prefect or orchestration job to fetch data from some source (reddit/twitter/...)

2. put it in s3

3. read from s3 and write a processing job to fine tune an LLM in sagemaker

4. deploy on sagemaker inference endpoint

5. wrap with a flask app

In my opinion from an ML/Data Science perspective an LLM is just like any other model. It's similar to how you would use BERT for something except that its like BERT on steroids.

I would look into using an API for open source models.

Here are some open source LLM vendors:

[perplexity.ai](http://perplexity.ai)

mistral

[abacus.ai](http://abacus.ai)

[replicate.com](http://replicate.com)

There's a ton more

If you need chatGPT3.5 level stuff it is effectively free in this day and age. GPT4 level stuff is where it gets pricey and mistral medium seems to be the best value LLM rn

If you need to fine tune I've seen <https://monsterapi.ai/> Never used it but might be worth looking into

- Ask thoughtful questions -- things that cant be googled. I like to ask about current challenges the team is facing
- When the HM talks about their job and duties, pay careful attention to this. Use this to ask thoughtful questions
- In my opinion, for internships, your soft skills are way more important bc the expectations for interns in terms of providing business value are much lower than regular engineers. Tho this varies widely between companies. cant speak for FAANG and big tech but I expect they have a higher level of hard skills, but in general your ability to learn and be mentored is whats key for internship interviews.

To wrap it up, being thoughtful, impressionable, and likeable goes a long way and it's important to focus on that. Send a small thank you note after the interview thanking them for their time and a small blurb on what you enjoyed learning about

I find simplify (<https://simplify.jobs/jobs?experience=Entry%20Level%2FNew%20Grad%3BJunior>) really great. Using the extension i can easily apply to 100 jobs in 2 hours

I've found LinkedIn not that great for looking for roles. It has a ton of ads, most of them are irrelevant or a bit shady contracting postings. It also doesnt recommend roles of the right level. I use boolean search to filter out senior roles to make it better. Here is the command I used when looking for ML roles

```
("machine learning" OR "ML" )(NOT "senior" NOT "sr" NOT "manager" NOT "lead" NOT "principal")
```

You can also do a google search with boolean search to search for greenhouse and [lever.co](http://lever.co)

I use something like this:

```
site:lever.co ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

ML Engineer Intern

From my experience, there's 2 avenues for SWE's to explore ML

1. **ML Platform:** Big tech companies like Uber have custom infra for training/testing/deploying ML models and need software engineers to build this. This would be a great segue point to shadow and work with ML teams

2. **ML Engineering/Data Infra:** ML especially in big tech companies have ML flows that are super segregated. This means that the folks who do feature engineering, and the folks who do training, and the folks responsible for deploying and monitoring are all distinct teams (unlike smaller companies where fewer people are responsible for the end-to-end pipeline).

   So, ML Engineering in big tech tends to refer to the more deploying and integrating ML models side. This is like 80% SWE and 20% ML. and the ML is generally domain understanding and it would make sense to try and transition to those teams

**The core idea is to get a SWE role in an ML team.** The core ML part (modeling) is only 10% of what ML teams do and generally in big tech these roles are handled by data scientists generally with advanced degrees. But since you're interested in the Engineering part it would make sense to look for SWE roles in the ML team. This transition should be similar to any other transition in SWE. Once you're there then I would look into upskilling. You might find that you dont need to be an ML wizard with expertise in PyTorch to be an MLE

Cant attach pics here but check out this <https://youtu.be/Z3-HddkYgyI?si=WX7BKREIe2zzoOlU&t=103> to get an idea of what is the breakdown of the roles. MLE seems to be basically "full stack" ML. But notice how the deploy and predict is handled by SWEs. So my suggestion is to look into roles at Uber for the deploy/predict part of the ML workflow

For more context on the ML at Uber, check out this [post ](https://www.linkedin.com/posts/b-sai_machinelearning-ml-ai-activity-7142957455877038080-ELuD?utm_source=share&utm_medium=member_desktop)I made on Linkedin breaking down Uber's ML platform.

I highly recommend this course: <https://www.ml.school/home>\
\
This course teaches you everything you need to know about ML that is not about training ML models, but rather ML in production. I am currently working through that course and it's awesome\
\
Feel free to DM on Slack (@Sai B) if you want to chat more about ML

Coming from someone who would like a mentor, I agree with the responses here, an important thing in my opinion is to find someone with a similar background/experience/shared struggles to yours

At the end of the day, we are all a product of our own experiences/struggles. Trying to mentor someone with a really different background/struggles can be challenging

Often when mentors are able to relate to what I'm struggling with, I notice that they work with me the best

In my experience, Leetcode style questions are not common for Data Science interviews. However, for MLE/Applied Scientist/ML Scientist they can occur sometimes.

For me personally, I am working more on the Engineering side, so I am trying to master a baseline level Leetcode competence (not trying to grind leetcode but atleast familiar enough so that if I have an interview with LC, I can spend 1-2 weeks brushing up what I need) by spending 3-5 hours a week on LeetCode\
\
Regarding data science interviews, I recommend this book: [**Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street**](https://a.co/d/8xzuY0f)

In my opinion, AI is great for getting 60% of the understanding in half the time. It is not a magic bullet that can answer all questions, but it definitively saves me time to give me a high level understanding of code, especially if the codebase is brand new

I get the best results when using GPT4, adding to the prompt things like "what is the purpose" or "please provide a high level takeaway"

Another note is there is a really really huge difference between gpt4 and gpt3.5 in my opinion

I view AI as a tool to accelerate your progress rather than a substitute for your work.

Using tools like github copilot and [cursor.sh](http://cursor.sh), it's worked really great for me when I need to jump into a code base I've never seen before and need to figure out where a certain logic is. These tools use something called semantic search which scans your entire codebase for the most relevant parts. I've used it before when I had an ML codebase with 20 ish files for a GAN model (one that I've never trained before) and had to figure out how to update a certain parameter, what a certain param does. Or to see what transforms are being applied to the images in the training pipeline

I will say though most AI coding tools only work well for python and in smaller projects

I can understand your experience. I feel like sometimes when you try to do something of everything you can get in decision paralysis.

At the very least you want to have a general foundation in the field you're targeting, (ML/SWE). Once you're there my suggestion is to make a list of priorities of your goals (I presume getting internship/new grad roles) and focus your attention on that and ignore the noise.

Since you already have a foundation, should you need to learn whatever you're feeling FOMO about you'll be able to learn fairly quickly.

This video by Steve Hyunh does an excellent job going in depth on this topic: <https://youtu.be/UAzQZdttdsA?si=m8GOkW82tL58VNQ5>

To summarize the video in a sentence: The goal should be a clear mind to think creatively and make good decisions, not perfect retention of facts

ML Engineer Intern @ Intradiem

Your post resonates with my experience. I'm also graduating this December as an international student with a Master's looking for MLE roles and I'm also finding it challenging to land interviews. Here's how I am approaching job searching:

**Create content on LinkedIn:** Create posts to distill ideas/lessons learned from projects you've worked on. I try to publish content that would have helped me if I had learned of this before starting the project. By doing so, you'll increase the visibility of your projects and can get more inbound DMs from recruiters. 

**Reach out to Hiring Managers/CEOs of startups:** I try to look for people/companies that are hiring on LinkedIn and reach out to get in touch and learn more about the company. The more relevance you have to the company the better it is. Relevance can be in terms of -- same city, same college, or is a similar field to some of your prior work

Software Engineering Intern at Series C Startup

testing

Starting at a new team or company? Or maybe you're just picking up a new tech stack? Go through these resources to learn how to ramp up fast.

[Taro Top 10] Effective Onboarding

Software engineers can have impact in many ways outside of coding, but make no mistake: Writing tons of very high-quality code quickly will always be useful. Follow the resources here to learn how to become a "code machine".