Member of Technical Staff, Robotics Research
Sieve, San Francisco
The role
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.
What you will do
- 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.