Research Scientist, Reinforcement Learning

Deeproute.ai; Colorado; On-site; USD 100,000 - 150,000; Full time

Job summary

Deeproute.ai is seeking a skilled professional to advance next-generation autonomous driving systems in Colorado. The candidate will apply reinforcement learning in safety-critical environments, focusing on training RL models and optimizing complex driving behaviors. Key skills include proficiency in modern RL algorithms and Python. Ideal candidates will have publications in top-tier venues and contributions to open-source RL projects. This role offers an opportunity to impact the future of autonomous driving technology significantly.

Qualifications

• 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.

Responsibilities

• Improve sim-to-real transfer for real-world robustness.
• Collaborate with cross-functional teams to integrate models into production systems.

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 in Python, comfortable with C++

Proficiency in deep learning frameworks such as PyTorch

Knowledge of sim-to-real transfer techniques and domain randomization

Knowledge of traffic rules, driving behavior modeling

Tools

Ray

Horovod

Job description

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

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 environmentsKnowledge 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