Member of Technical Staff, Robotics Research

• 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.

• 4+ years of research or engineering experience in robot learning, embodied AI, or 3D computer vision. A PhD is welcome but not required; strong publications, open-source projects, or systems you can walk us through count just as much.
• Fluent Python plus PyTorch or JAX: you can write, debug, and change training code yourself
• Hands-on 3D vision work: multi-view geometry, depth, pose, tracking, or reconstruction
• Have trained a policy or world model (VLA, diffusion policy, imitation learning, action-conditioned video model, etc.) and tested it on data it never saw in training
• Rigorous about experiments: you set baselines, run ablations, and can tell whether a gain came from the data or from architecture, compute, or a leaky eval
• Comfortable with large, messy datasets and diagnosing data quality issues
• Strong communication skills and the ability to explain findings, tradeoffs, and uncertainty clearly
• In-person at our SF HQ

• Robot learning from egocentric or human video, including retargeting human motion onto robot embodiments
• Have put a trained policy on real robot hardware and measured it in closed loop
• Simulators like MuJoCo or Isaac Sim, used for evaluation or synthetic data
• Large-scale distributed training
• Experience as an early hire at a startup

• 401(k) and full health insurance
• Breakfast, lunch, and dinner covered, plus your choice of snacks
• Ubers covered home

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

Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data.

We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as Matrix Partners https://matrix.vc/, Swift Ventures https://www.swift.vc/, Y Combinator https://www.ycombinator.com/, and AI Grant https://aigrant.com/.