Linguistic Engineer

Meta

Meta builds technologies that help people connect, find communities, and grow businesses.

Burlingame, CA, USA

$103,000 - $156,000

Machine Learning

Mid-Level Software Engineer

In-Person

5,000+ Employees

2+ years of experience

AI · AR/VR

Description For Linguistic Engineer

Meta's AR/VR division is at the forefront of innovation in augmented and virtual reality technology. As a Linguistic Engineer, you'll play a crucial role in developing and maintaining the foundation of our machine learning systems, particularly in speech recognition and natural language understanding.

Your primary focus will be on creating high-quality datasets and ensuring consistent model performance across multiple languages, directly impacting the effectiveness of our voice assistant products. This position offers a unique blend of computational linguistics and machine learning, requiring both technical expertise and linguistic understanding.

Working within Meta's AR/VR team, you'll collaborate closely with machine learning engineers, researchers, and product teams to advance our conversational AI capabilities. The role demands a strong foundation in programming and data analysis, with opportunities to work with cutting-edge technology in natural language processing and machine learning.

This position is ideal for someone who combines technical skills with linguistic expertise and has a passion for improving human-computer interaction. You'll be part of a team that's shaping the future of communication in virtual and augmented reality environments.

The role offers competitive compensation, ranging from $103,000 to $156,000 annually, and is based in Burlingame, CA. You'll be working with state-of-the-art tools and technologies, including Python and various machine learning frameworks, while contributing to products that will define the next generation of social technology.

Join Meta's AR/VR division to be part of a team that's pushing the boundaries of what's possible in language technology and artificial intelligence. Your work will directly impact how millions of users interact with Meta's AR/VR products through natural language interfaces.

Last updated 3 months ago

Responsibilities For Linguistic Engineer

Creating datasets
Maintaining consistent models across languages
Ensuring voice assistant product functions effectively
Collaborating with machine learning teams
Contributing to conversational AI advancement
Delivering datasets, models, and knowledge for machine learning systems
Working on speech recognition and natural language understanding components

Requirements For Linguistic Engineer

Python

PHP

At least two years of experience as a data scientist, software engineer, or computational linguist
Experience with programming and data analysis in Python and SQL
Familiarity with text analysis and databases
Preferred: Five or more years of experience
Preferred: Knowledge of relationship between data and machine learning models
Preferred: Fluency in additional languages
Preferred: Advanced coursework in relevant fields

Meta

Meta builds technologies that help people connect, find communities, and grow businesses.

Burlingame, CA, USA

$103,000 - $156,000

Machine Learning

Mid-Level Software Engineer

In-Person

5,000+ Employees

2+ years of experience

AI · AR/VR

Meta

Snapshot Array

Data Structures & AlgorithmsMedium

Implement a SnapshotArray that supports the following interface: SnapshotArray(int length) initializes an array-like data structure with the given length. Initially, each element equals 0. void set(index, val) sets the element at the given index to be equal to val. int snap() takes a snapshot of the array and returns the snap_id: the total number of times we called snap() minus 1. int get(index, snap_id) returns the value at the given index, at the time we took the snapshot with the given snap_id For example: Input: [SnapshotArray,set,snap,set,get] [[3],[0,5],[],[0,6],[0,0]] Output: [null,null,0,null,5] Explanation: SnapshotArray snapshotArr = new SnapshotArray(3); // set the length to be 3 snapshotArr.set(0,5); // Set array[0] = 5 snapshotArr.snap(); // Take a snapshot, return snap_id = 0 snapshotArr.set(0,6); snapshotArr.get(0,0); // Get the value of array[0] with snap_id = 0, return 5 What data structures and algorithms are best suited for this problem? What are the time and space complexities of each operation?

Arrays

Binary Search

Meta

Design a URL shortening system like TinyURL.

System DesignMedium

Let's design a system for URL shortening, like TinyURL. Consider the following: Functional Requirements: Users should be able to enter a long URL and get a shorter URL. Users should be able to access the original URL by visiting the shortened URL. The system should be highly available and handle a large number of requests. Analytics: Track the number of times a shortened URL is accessed. Non-Functional Requirements: The system should have low latency for both URL shortening and redirection. The shortened URLs should be relatively short. The system should be reliable and fault-tolerant. Considerations: How would you handle collisions (when two different long URLs map to the same short URL)? How would you design the data store to efficiently handle a large number of URLs? How would you scale the system to handle a large number of requests? What algorithms could be used for generating the short URLs? Discuss the trade-offs. Walk me through the architectural components, data model, and scaling strategies for this URL shortening service. For instance, if a user inputs https://www.example.com/very/long/path/to/resource, the system might generate a short URL like https://short.url/xyz123. When a user visits https://short.url/xyz123, they should be redirected to the original URL.

Arrays

Strings

Database Problems

Graphs

Dynamic Programming

Greedy Algorithms

Bit Manipulation

Meta

Tell me about a time you disagreed with a manager.

Behavioral

Tell me about a time you disagreed with a manager. What was the situation, and how did you handle it? What was the outcome, and what did you learn from the experience? For example, perhaps you had a disagreement about project priorities, a technical approach, or a team management style. It could be a situation where you felt strongly about a different course of action and had to navigate the disagreement professionally. Describe the specific steps you took to communicate your perspective, understand your manager's viewpoint, and reach a resolution. What factors did you consider in deciding how to approach the situation, and what alternative approaches did you consider? How did you ensure that the disagreement didn't negatively impact your working relationship or the overall team dynamic? What would you do differently in a similar situation in the future?

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