We are seeking exceptional Senior Machine Learning and Simulation Engineers to join NVIDIA's Autonomous Vehicles (AV) Simulation team! This role requires strong technical leadership and outstanding software engineering skills, coupled with deep expertise in both simulation and artificial intelligence, including deep learning, reinforcement learning, end-to-end driving and Physics AI models. The successful candidate will have a solid track record of productizing ML solutions for autonomous driving and simulation at scale.
This position centers on developing a Closed-Loop Simulation-based Reinforcement Learning (RL) framework in order to train advanced end-to-end AV models, such as Alpamayo R1. This position will design and improve the accuracy and performance of the RL framework and simulation, leveraging SOTA techs including NuRec, Traffic Models, and Cosmos World Model. Success in this role requires close collaboration with the AV Platform, AV Product, and Research teams.
What you will be doing:
- Lead the design and development of large-scale RL training frameworks to accelerate the development of multi-modal AV foundation models.
- Design, build, and optimize simulation and data processing pipelines to enable scalable training of driving policies.
- Focus on measuring and enhancing simulation quality and refining the reward function for RL training.
- Ensure the reliability and performance of training workflows on large GPU clusters through the development of robust monitoring and debugging tools.
- Partner with researchers to integrate state-of-the-art model architectures into efficient and scalable training pipelines.
What we need to see:
- Bachelor's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience).
- 12+ years of relevant professional experience encompassing large-scale ML training, AV systems, simulation, and AI infrastructure development.
- Deep proficiency in RL algorithms, such as PPO and GRPO, including practical experience with hyperparameter tuning and reward function design.
- Exceptional programming skills in C++ and Python, vital for developing efficient systems and data pipelines.
- Extensive experience with large-scale GPU clusters, High-Performance Computing (HPC) environments, and job scheduling/orchestration tools (e.g., Kubernetes, SLURM).
Ways to stand out from the crowd:
- Experience in RL infrastructure or general LLM training/fine-tuning infrastructure in industry.
- Experience in simulation & closed-loop evaluation of autonomous driving end-to-end models.
- Proven record on large-scale data pipeline development and algorithm optimization.
#AutonomousVehicles
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor's degree in Computer Science, Robotics, Engineering, or a related field, or equivalent experience
- 12+ years of relevant professional experience in large-scale machine learning training, autonomous vehicle systems, simulation, and AI infrastructure development
- Deep proficiency in reinforcement learning algorithms such as PPO and GRPO, including hyperparameter tuning and reward function design
- Exceptional programming skills in C++ and Python
- Extensive experience with large-scale GPU clusters and High-Performance Computing environments
- Experience with job scheduling or orchestration tools such as Kubernetes or SLURM
- Experience in reinforcement learning infrastructure or LLM training and fine-tuning infrastructure in industry
- Experience in simulation and closed-loop evaluation of autonomous driving end-to-end models
- Proven record developing large-scale data pipelines and optimizing algorithms
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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