Research Scientist, Reinforcement Learning for Autonomous Planning and World Modeling
Autonomous driving technology company running a fully autonomous ride-hail service, Bay Area
| Employer | Autonomous driving technology company running a fully autonomous ride-hail service, Bay Area (not named) |
|---|---|
| Location | Mountain View, CA (Bay Area) · Hybrid |
| Type & level | Full-time · Senior |
| Pay | USD 204k–259k base Source: pay range stated in the source posting |
| Visa path | O-1A fit |
| Role family | Autonomous Vehicle Perception & Planning Engineer |
About the role
This role sits on an AI foundations research team working on open machine learning problems in autonomous driving, including reinforcement learning and generative modeling. The scientist helps post-train and evaluate a large world model and develops RL and distillation methods for trajectory planning.
What you'll do
- Contribute to post-training and evaluation of a foundation world model
- Research and build reinforcement learning and distillation techniques for vehicle trajectory planning
- Bring emerging academic and industry methods into the internal RL infrastructure
- Run rigorous ablations to find and scale the most promising methods
- Share training recipes and post-training practices with engineering and research teams
Requirements
- PhD or Master's in computer science, machine learning, robotics or a similar field
- 3+ years of industry or post-doc research experience in reinforcement learning or foundation models
- Original contributions shown through publications, technical posts or open-source work
- Ability to implement scalable distributed training such as data parallel and FSDP or other sharding
- Willingness to work with globally distributed inference infrastructure
Nice to have
- PhD focused on reinforcement learning, foundation models or multi-modal learning
- Experience building RL infrastructure for on-policy learning or human-preference alignment
- First-author papers at top venues or maintenance of significant open-source ML projects
- Experience with many-machine training and large-model inference techniques such as tensor parallelism
Pay & benefits
USD 204k–259k base. The posting lists health, dental, vision, life and disability insurance, a 401(k) match, paid time off and holidays, parental leave, and eligibility for a discretionary bonus and equity plan.
Pay source: pay range stated in the source posting. Market data, not an offer from O1DMatch.
Visa path
Research scientists commonly use first-author publications, citations, peer review service, open-source impact and high compensation as O-1A evidence. The posting states no citizenship restriction. An employer like this or a U.S. agent can file, and approval depends on the individual's record.
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