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.

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.

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

Member of Technical Staff, Computer Vision (Perception)

Responsibilities

Qualification

Required

Benefits

Company

Sieve

More roles at Sieve