Deep proficiency in rl algorithms like ppo and grpo
12+ years of professional experience in ml and av systems
Exceptional programming skills in c++ and python
This role centers on developing a Closed-Loop Simulation-based Reinforcement Learning framework to train advanced end-to-end autonomous vehicle models
Job Summary
This role centers on developing a Closed-Loop Simulation-based Reinforcement Learning framework to train advanced end-to-end autonomous vehicle models.
The successful candidate will lead the design of large-scale RL training frameworks to accelerate the development of multi-modal AV foundation models.
Candidates are eligible for equity and benefits, with base salary ranges from $224,000 to $431,250 USD depending on level.
Matching Summary
This role centers on developing a Closed-Loop Simulation-based Reinforcement Learning framework to train advanced end-to-end autonomous vehicle models.
Salary
Base: $224,000 - $356,500 (Level 5) / $272,000 - $431,250 (Level 6); Bonus/Equity: Eligible for equity; Benefits: Eligible for benefits
Skills & Requirements
Must-have
Deep proficiency in RL algorithms like PPO and GRPO
12+ years of professional experience in ML and AV systems
Exceptional programming skills in C++ and Python
Extensive experience with large-scale GPU clusters and HPC
Experience with Kubernetes and SLURM job scheduling
Nice-to-have
Experience in RL infrastructure or LLM training pipelines
Proven record on large-scale data pipeline development
Experience integrating state-of-the-art model architectures
Key Requirements
Bachelor's degree in Computer Science, Robotics, Engineering, or related field
12+ years of relevant professional experience
Deep proficiency in hyperparameter tuning and reward function design