About the RolePhysical AI runs on data that captures how the real world looks, moves, and fits together in 3D. As a perception-focused Machine Learning Engineer at Sieve, you'll build the systems that turn raw egocentric, robot, and sensor footage into the training data that robotics and world-model teams depend on.
You'll bring the computer vision instincts of someone who has shipped perception for robots, devices, or autonomous vehicles, and apply them to datasets at a scale few teams ever touch. One week you might recover camera pose and scene geometry across thousands of hours of first-person video. The next, you might build a hand and object tracking pipeline, or design a benchmark that tells a lab whether their data actually improves manipulation policies.
What You'll Do- Build perception pipelines for depth, pose, tracking, segmentation, and 3D reconstruction over large video and multi-sensor datasets
- Apply SLAM, visual odometry, and structure-from-motion to recover camera trajectories and scene geometry at scale
- Fine-tune vision and vision-language models for embodied tasks like hand-object interaction, activity understanding, and scene understanding
- Develop automated QA and filtering systems that catch calibration errors, sync drift, motion blur, and other data defects before they reach customers
- Create benchmarks and evaluation frameworks that measure how much a dataset improves downstream robotics and world-model performance
- Work directly with frontier labs and robotics teams to turn vague data requirements into production pipelines
- Ship fast, measure results, and iterate based on how the data performs in the real world
Requirements- 3+ years building production perception or computer vision systems for robotics, AR/VR devices, autonomous vehicles, or similar
- Hands-on experience with SLAM, visual-inertial odometry, multi-view geometry, or 3D reconstruction
- Strong Python skills and fluency with PyTorch; C++ is a plus
- Comfortable with multi-sensor data such as camera intrinsics and extrinsics, IMU, depth, and time synchronization
- Experience training, fine-tuning, or deploying modern deep learning models for vision
- Sharp intuition for evaluation, edge cases, and what makes data actually useful for training
- Comfortable owning a problem from the customer conversation to a deployed pipeline
- Excited about Physical AI and the data that will power it
- In person at our SF HQ
Bonus- Experience with neural scene representations such as NeRFs or Gaussian splatting
- Background working with egocentric or teleoperation data
- Publications or open-source work in perception, 3D vision, or robotics
All roles at Sieve require you to be onsite in San Francisco 5 days per week.
