Research Scientist, Reinforcement Learning
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Before you apply
SalaryNo pay range posted
LocationFremont, California, United States
Company size4+ people
PostedJun 6, 2026 · 5mo ago
Verified live13h ago
May no longer be actively hiring
Posted by the company on Jun 6, 2026No pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
Proficiency in modern RL algorithms: DQN, PPO, SAC, TD3, etc.
Proficiency in modern RLHF algorithms: PPO, DPO, GRPO, etc.
Hands-on experience training reward models and finetuning LLM/VLM/VLA
Knowledge of distributed RL training at scale
Proficiency with massively parallel simulation environments
Knowledge of sim-to-real transfer techniques and domain randomization
Proficiency in Python, comfortable with C++
Proficiency in deep learning frameworks such as PyTorch
Experience with distributed training frameworks (Ray, Horovod, etc.)
Knowledge of traffic rules, driving behavior modeling
Knowledge of model optimization (quantization, pruning) and CUDA is a plus
Publications in top-tier venues (ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, etc.)
Open-source contributions to RL libraries or autonomous driving projects
Previous experience with LLM fine-tuning using RLHF
Knowledge of safe RL, interpretable AI, or robustness techniques
Familiarity with autonomous vehicle regulations and safety standards
Skills
Python
C++
PyTorch
Ray
Horovod
CUDA
About the role
We are building next-generation end-to-end autonomous driving systems powered by reinforcement learning.
You will work on applying RL in closed-loop, safety-critical environments, leveraging large-scale simulation and real-world driving data to improve safety, comfort, and robustness.
Responsibilities
•
Train and deploy RL policies in closed-loop driving environments
•
Scale RL training using massively parallel simulation systems
•
Design and optimize reward functions for complex driving behaviors
•
Improve sim-to-real transfer for real-world robustness
•
Collaborate with cross-functional teams to integrate models into production systems
Requirements
Core Technical Skills
•
Proficiency in modern RL algorithms: DQN, PPO, SAC, TD3, etc.
•
Proficiency in modern RLHF algorithms: PPO, DPO, GRPO, etc.
•
Hands-on experience training reward models and finetuning LLM/VLM/VLA
•
Knowledge of distributed RL training at scale
•
Proficiency with massively parallel simulation environments
•
Knowledge of sim-to-real transfer techniques and domain randomization
•
Proficiency in Python, comfortable with C++
•
Proficiency in deep learning frameworks such as PyTorch
•
Experience with distributed training frameworks (Ray, Horovod, etc.)
•
Knowledge of model optimization (quantization, pruning) and CUDA is a plus
•
Knowledge of traffic rules, driving behavior modeling
Preferred Qualifications
•
Publications in top-tier venues (ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, etc.)
•
Open-source contributions to RL libraries or autonomous driving projects
•
Previous experience with LLM fine-tuning using RLHF
•
Knowledge of safe RL, interpretable AI, or robustness techniques
•
Familiarity with autonomous vehicle regulations and safety standards