Research Scientist, Reinforcement Learning
At a glance
Mid-level AI Research role at Deeproute.ai. Fremont · no salary stated · we estimate $135,000–$232,000 across the United States.
Deeproute.ai has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Train and deploy reinforcement-learning policies in simulated driving loops, then scale training through parallel simulation
- Shape reward functions for complex driving actions and improve how policies transfer from simulation to road data
- Work with other teams to bring trained models into production systems
- Use simulation and real driving data to strengthen safety, comfort and robustness
What you bring
- You know current RL methods such as DQN, PPO or SAC, plus RLHF approaches such as DPO or GRPO
- You have trained reward models and fine-tuned LLM, VLM or VLA models hands-on
- You understand distributed RL at scale, parallel simulation and sim-to-real methods
- You can build in Python, work comfortably in C++, and use PyTorch or another named deep-learning framework
Who this fits
This suits a mid-level research scientist focused on reinforcement learning for closed-loop driving. The role is onsite in Fremont. You may also bring preferred strengths such as research publications, open-source work, RLHF fine-tuning, safe RL or autonomous-vehicle safety knowledge.
From the employer
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.
- 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