Senior Computer Vision Engineer

Own core computer vision models, from 3D human pose to statistical body modeling, taking them from prototype to production;

Ship those models to run real-time in the cloud and on-device on tablets, owning the conversion and optimization in between;

Own the data and code lifecycle behind them: training frameworks, annotation workflows, pipelines, auto-labeling, test sets and taxonomy;

Extend movement understanding into multimodal territory, combining it with language and reasoning; build novel approaches in the movement-intelligence domain;

Unify and mature how we train, track, version and deploy models, so every result is reproducible and testable;

Design systems that run without you, automating the loops so the team's output scales past manual effort;

Help grow the Computer Vision team by defining and promoting best practices, establishing principles that scale your impact.

5+ years solving complex problems with Computer Vision, with models shipped to production;

Strong software engineering foundation across architecture, pipelines, MLOps and the full model lifecycle;

Deep, hands-on command of modern Computer Vision (transformers and convolutional models), with real depth in at least one of detection, segmentation, tracking, pose, or 3D;

A data-centric instinct: you cook your own data, build data flywheels and active-learning loops, and treat the dataset as source code;

Solid grounding in multimodal AI, with the ability to build systems that pair vision with language and reasoning when the problem calls for it;

Strong written, asynchronous communication. You think in specs and documents and leave a clear trail others can build on;

Self-direction and full ownership: you scope ambiguity into a plan and ship without being told;

Fluency with PyTorch or JAX, the Python data stack, and comfort picking up new languages and codebases;

AI-native ways of working: you build AI into the workflow itself, from agents to LLM-in-the-loop tooling.

Experience with pose estimation, body modeling, movement intelligence, or human-motion work;

Experience working with massive data, building full-lifecycle ML systems at a startup or scaleup, wearing different hats;

Experience automating experimentation or model-improvement loops;

Product mindset, users empathy and desire to ship impact to millions of members.