The OpportunityAs a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated.
You'll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You'll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets.
Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence.
What You'll Do- Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
- Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
- Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
- Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
- Develop tooling for benchmarking, observability, and temporal efficiency
- Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback
What We're Looking For- Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work
- Engineers who want their work to directly impact the next frontier of physical AGI
- Strong ML engineering fundamentals across robotics, computer vision, and perception systems
- Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
- Strong technical intuition and ability to move quickly in ambiguous research environments
- Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus
