Senior Machine Learning and Simulation Engineer, Autonomous Vehicles

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $224,000–$356,500 / yr
Posted: 13 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $219k

This role $290k

$160k most similar roles pay here $378k

This role pays more than 93% of similar roles. Most pay $182,712–$254,750 — the shaded band above. At the midpoint, this role pays about $290k versus about $219k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Machine Learning and Simulation Engineer, Autonomous Vehicles

Role Posting Log in to save

Senior Machine Learning and Simulation Engineer - Autonomous Vehicles will join the Autonomous Vehicles Simulation team to lead the design and development of large-scale reinforcement learning training frameworks for multi-modal autonomous vehicle foundation models. The role involves building and optimizing simulation and data processing pipelines, refining reward functions, and ensuring reliable performance on large GPU clusters through robust monitoring tools. Key responsibilities include integrating state-of-the-art architectures into scalable pipelines while improving the accuracy of closed-loop simulations. Candidates must possess deep expertise in reinforcement learning algorithms like PPO and GRPO, along with proficiency in C++, Python, and high-performance computing environments using Kubernetes or SLURM. The work focuses on solving technical challenges in training advanced end-to-end driving models by leveraging NuRec, Traffic Models, and Cosmos World Model within a specialized simulation framework.

Skills

Reinforcement Learning Python C++ Deep Learning GPU Clusters Kubernetes SLURM High-Performance Computing Data Pipelines Simulation Closed-Loop Simulation PPO GRPO NuRec Cosmos World Model

What you'll do

Lead the design and development of large-scale RL training frameworks for multi-modal AV foundation models.
Build and optimize simulation and data processing pipelines to enable scalable training of driving policies.
Measure and enhance simulation quality while refining reward functions for reinforcement learning.
Develop robust monitoring and debugging tools to ensure reliable performance on large GPU clusters.
Integrate state-of-the-art model architectures into efficient, scalable training pipelines in partnership with researchers.
Develop high-performance data pipelines and optimize algorithms for autonomous driving systems.
Implement advanced RL algorithms including PPO and GRPO with a focus on hyperparameter tuning.

What we're looking for

Bachelor's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience).
12+ years of professional experience in large-scale ML training, AV systems, simulation, and AI infrastructure development.
Deep proficiency in RL algorithms including PPO and GRPO with experience in hyperparameter tuning and reward function design.
Exceptional programming skills in C++ and Python for developing efficient systems and data pipelines.
Extensive experience with large-scale GPU clusters, High-Performance Computing (HPC) environments, and job scheduling tools like Kubernetes or SLURM.
Proven track record of productizing ML solutions for autonomous driving and simulation at scale.
Experience in RL infrastructure or LLM training/fine-tuning infrastructure is preferred.
Experience in simulation and closed-loop evaluation of autonomous driving end-to-end models is preferred.

Save