Member of Technical Staff, Robotics Research

Sieve is hiring its first Robotics Researcher to lead training and evaluation of world-action models and 3D perception systems. The role investigates how data affects robot capabilities and translates research findings into datasets, pipelines, and guidance for partner labs.

Responsibilities:

• Data ablations for robot learning: Compare data sources such as egocentric human video, teleop, and robot rollouts, and vary quality, mix, embodiment, and supervision to learn which choices improve policies and world models
• World-action models: Train and post-train action-conditioned world models and policies, pushing them toward skills like following commanded actions, manipulating objects, and responding to camera motion
• 3D labeling from video: Build pipelines for depth, camera trajectories, hand/body/object pose, point tracks, and scene reconstruction, so raw footage becomes action-labeled data grounded in 3D
• Evaluation design: Lock the test tasks, environments, and embodiments before comparing models, and report clear, checkable conclusions about which data made the difference
• Simulation as a tool: Use simulators or learned world models for evaluation, augmentation, and sim-to-real checks
• From findings to datasets: Turn results into the curation filters, annotation specs, and capture protocols behind our pipelines and the data we deliver to partner labs
• Partner collaboration: Work with Sieve engineers and researchers at partner labs to pick the right problems and share what you find
• Publish what you learn: Share findings through papers, technical reports, blog posts, and open datasets or benchmarks where it makes sense, and help shape how the field thinks about data for robot learning