Responsibilities

As our first Robotics Researcher at Sieve, you'll lead the training and evaluation of world-action models (WAMs) and 3D perception systems to understand how data shapes robot capabilities. Your work connects how we capture, curate, and label physical-world data to measurable gains in robot learning.
You'll run your projects from question to answer: spot where a model falls short, decide what data might close the gap, set up the experiment, and test the result on tasks and environments the model hasn't seen. A lot of this work is in 3D. Raw multimodal and egocentric video becomes useful for robot learning only once it carries geometry, pose, and action, and you'll build the systems that add them. Simulation is a tool you'll use when it makes evaluation or augmentation cheaper, not the center of the job.
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.

Member of Technical Staff, Robotics Research at Sieve · LumiJobs

Member of Technical Staff, Robotics Research

Responsibilities

Qualification

Required

Benefits

Company

Sieve

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