Deeproute.ai

Licenses autonomous driving systems to OEMs

Research Scientist - Reinforcement Learning

AI ResearchConfirmed live in the last 24 hours

Full-timeFremont, CA, USANo salary listedIn-person

About the Job

Requirements

Proficiency in modern reinforcement learning algorithms, including DQN, PPO, SAC, and TD3.
Proficiency in modern reinforcement learning from human feedback algorithms, including PPO, DPO, and GRPO.
Hands-on experience training reward models and fine-tuning large language models, vision-language models, and vision-language-action models.
Knowledge of distributed reinforcement-learning training at scale.
Proficiency with massively parallel simulation environments.
Knowledge of sim-to-real transfer techniques and domain randomization.
Proficiency in Python and comfort with C++.
Proficiency with deep learning frameworks such as PyTorch.
Experience with distributed training frameworks such as Ray and Horovod.
Knowledge of traffic rules and driving behavior modeling.

Responsibilities

Train and deploy reinforcement-learning policies in closed-loop driving environments.
Scale reinforcement-learning 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.

Desired Qualifications

Knowledge of model optimization techniques such as quantization and pruning, and CUDA.
Publications in top-tier venues, including ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, and IROS.
Open-source contributions to reinforcement-learning libraries or autonomous-driving projects.
Previous experience with large language model fine-tuning using reinforcement learning from human feedback.
Knowledge of safe reinforcement learning, interpretable artificial intelligence, or robustness techniques.
Familiarity with autonomous-vehicle regulations and safety standards.

Research Scientist, Reinforcement Learning

Deeproute.ai · Fremont, United States · — · Posted 2026-06-06

Workplace: on_site

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

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

Apply

Apply at Deeproute.ai

About the Company

Deeproute.ai

Licenses autonomous driving systems to OEMs

Deeproute.ai develops smart driving solutions for the automotive industry, offering Level 2 ADAS and Level 4 autonomous driving for robotaxis and medium-duty trucks. Its DeepRoute-Driver and DeepRoute IO platform use an end-to-end model that combines perception, prediction, and planning, and it relies on map-free navigation that does not require HD maps. The company licenses its autonomous systems to OEMs on a per-vehicle basis and differentiates itself with map-free technology, a dual-track strategy, and partnerships with automakers like Dongfeng, Geely, Great Wall, and CaoCao Mobility. Its goal is to scale the installed base from thousands to about 200,000 vehicles by 2025, expanding through robotaxi services and fleet operations.

Company size: 51-200
Company stage: Series C
Total funding: $450M
Headquarters: Shenzhen, China
Founded: 2019

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