Member of Technical Staff, Computer Vision (Perception)

Physical 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. All roles at Sieve require you to be onsite in San Francisco 5 days per week.

Responsibilities

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

Nice to have

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

Benefits

401k; Full Health Insurance; Breakfast, Lunch, and Dinner covered; Choice of snacks; Ubers covered home