World Labs
Research Engineer / Scientist (Robot Learning)
San Francisco · Senior
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Chemical Engineers (17-2041) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- This role is focused on training end-to-end policies with an emphasis on sim-to-real transfer, robust performance, scalable training and inference pipelines.
- This is a hands-on, research-driven role for someone working at the intersection of robotics and machine learning.
Responsibilities
- Develop and improve training pipelines for sim-to-real transfer, including domain randomization, system identification, real-sim alignment.
- Build end-to-end training and evaluation workflows for robot policies, from large-scale data generation to scale up training and evaluation.
- Optimize policy performance across the stack, including training speed, inference latency, data generation efficiency to support large scale production constraints.
- Diagnose failure modes in simulation and real-world rollouts, design principled solutions to improve robustness, efficiency and generalization.
- Strong foundation in robotics, neural network designs, sim-real transfer.
Requirements
- 6+ years of experience working on manipulation, locomotion, robot policy training, or related areas.
- Deep experience with robot policy designs (e.g., VLA, WAM, diffusion).
- Proficiency in Python and/or C++, with hands-on experience building research or production robotic systems.
Compensation
- $250,000 - $350,000 base salary (good-faith estimate for San Francisco Bay Area upon hire
- Total Compensation
- Base salary plus equity awards
- Salary History
- We do not request or consider prior compensation in making offers
- Cal. Lab. Code §1197.5 (Equal Pay Act)
Benefits
- Design and implement modern robot learning systems, including imitation learning, reinforcement learning for manipulation.
- Collaborate with simulation and infrastructure teams to minimize sim-to-real gap and ensure learning methods integrate cleanly with real-robot deployment stacks.
- Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for robot learning across the organization.
- Experience with deep learning frameworks (e.g., PyTorch) and low-level robotic controller.
Equal opportunity
- World Labs is an equal opportunity employer.
- We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law.
- We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
- California Pay Transparency
This listing is sourced directly from World Labs's careers page and normalized into a canonical job model.