Suggestions for good open-source AI projects I can contribute to?

I was in your same position exactly a year ago, so this hits really close to home. It was so hard to get interviews that even with spam applying, I only got 2 callbacks in a month. Then I started contributing to open-source, and things started looking up. Here's some pointers:

**For those who are later in their career, I think these principles still apply. The impact of doing this earlier in your career has more of a 0-1 effect than the 1-5 effect someone later in their career would see IMO

Pick A Niche

So there's a deluge of MSCS graduates who want to work in AI. You need to focus on a single direction. There's several routes you can take:

• Model Serving
• Classic ML model development (collaborative filtering, etc)
• Large ML models (LLMs, stable diffusion, etc)
• Fine-tuning
• Generative AI tools
• ML infra tools like Flyte/Airflow
• Classic ML model dev and Large Model work typically require top conference papers to be in the top 1%

Before you dive into any route, make sure you do some due diligence so that you know roughly what it takes to be the top 1% in anything. Then decide if you're willing to put in the work.

Pick The Project Size

Then there's size of the open source project. Small libraries/frameworks are too unknown to get you any real attention with recruiters. Large libraries/frameworks will take 1-2 years to get on the PMC.

Small projects are things like util libraries. Large projects are established frameworks like PyTorch or Kubernetes.

The "goldilocks zone" IMO is medium-size projects that are rapidly gaining traction. They have enough things on their roadmap that'll add immediate value and they should be the most welcoming of new contributors.

Making Core Contributions

Start with something small. No sane person would ever let a stranger add changes to their core codebase. Start with something small like a cherry-pick commit or an integration.

Then it's time to align your contributions to the actual product roadmap. Attend contributor meetings. Talk to other contributors on Slack/Discord. Get involved.

As you contribute, you'll see yourself go up the "top contributors" list. You'll see yourself writing better PRs, writing cleaner code, and working on more impactful features.

Spinning Open-source Into Opportunities

Getting to the top 10 or top 5 contributors for a medium-size library doesn't take that long. Even in the span of 1-2 months, you can get to Top 5 if you put in full-time effort.

Now you have some awesome résumé bullet points:

• Built core features: X, Y, Z, etc
• Top 5 contributor for Github project with 20k stars and 500K downloads/month

Now go! Find a project and contribute. You'll learn a lot + be helping out the community. I think that you can only re-write your resume so many times before you need to make a fundamental change. Making open-source contributions can help you make that fundamental change to your profile.

TensorFlow is open-source and widely used: https://github.com/tensorflow/tensorflow

The problem is that the repo is huge (2.8k pull requests with 185k GitHub stars), so getting something merged in is probably super hard 😥

The "How to start contributing to open source?" discussion is particular helpful for high-level advice. In particular, I recommend working on a repo you use yourself.

We also recently shipped an open-source contribution course with the former Director of Open Source Engineering at Facebook. You can watch it here: [Course] Become An Open Source Master