Computer Vision Engineer
MetaTrack this application
Get Started FreeMatch score against your CV
Get Started FreeTailor your resume to this job
Get Started FreeAbout interviewing at Meta
Recruiter screen, a technical screen (60-minute asynchronous challenge or paired live coding), a short culture-fit questionnaire, then a virtual onsite of about four rounds: fast-paced coding with multiple problems per round, system design scoped to level, a lighter behavioral, and an AI-enabled coding round assessing how you work with a coding assistant.
Read the full Meta interview process →Description
About
Meta Reality Labs is seeking a Machine Learning Engineer to drive the productization of gesture recognition models for our AR/VR devices. This role bridges research and production—you'll take ML models from development through deployment, collaborating across teams to ship gesture-based interactions on Quest and Meta Glasses. This is not a pure research position; we're looking for someone who thrives at turning cutting-edge ML into real products that reach millions of users.
Responsibilities
- Own end-to-end development of gesture recognition ML models from prototyping to production deployment on AR/VR devices
- Collaborate with cross-functional teams including research, hardware, product, and platform engineering to integrate models into shipping products
- Optimize ML models for on-device performance, balancing accuracy, latency, and power constraints
- Partner with Reality Labs Research (RL-R) to translate research breakthroughs into production-ready solutions
- Define technical direction and mentor engineers on ML productization best practices
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or equivalent practical experience
- Experience shipping ML models to production
- Background in computer vision or machine learning, with focus on areas such as gesture recognition, pose estimation, or object tracking
- Experience with model optimization for edge/on-device deployment
- Track record of cross-functional collaboration to deliver ML-powered features Experience deploying ML models on edge devices
- Experience with gesture recognition, hand tracking, or human pose estimation
- Background in real-time inference, model compression, or quantization techniques
- 5+ years of industry experience in machine learning or computer vision
- MS or PhD in Computer Science, Machine Learning, Computer Vision, or related field
- Publication track record at conferences such as CVPR, NeurIPS, ECCV, ICCV, ICML