- BS or higher in Computer Science, Robotics, Electrical Engineering, or a related field (or equivalent experience).
- 12+ years of experieence in the related field.
- Solid background in Reinforcement Learning, including policy gradient methods (PPO, GRPO), actor-critic architectures, on-policy and off-policy RL
- Proficiency in PyTorch or TensorFlow and real experience with RL-related algorithm
- Experience in C++ and Python development for real-time systems.
- Strong analytical and problem-solving skills, with a track record of implementing and debugging complex RL systems.
Ways to stand out from the crowd
- Background in shipping autonomous driving features or embodied AI.
- Experience with generative models (Flow Matching, Diffusion, or AR-based decoders) in the context of policy representation or trajectory modeling.
- Experience with training policies on their own rollout distributions and handling the compounding error problems inherent in autonomous driving.
- Experience working with large-scale data flywheels, including mining scenarios from fleet telemetry logs, auto-labeling pipelines, and automated performance tracking.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.