Senior Research Engineer, Autonomous Vehicles

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 83 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 $209k

This role $236k

$153k $302k

below market most similar roles pay here above market

This role pays more than 72% of similar roles. Most pay $168,942–$248,375 — the blue band above. At the midpoint, this role pays about $236k versus about $209k 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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Research Engineer, Autonomous Vehicles

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The Senior Research Engineer - Autonomous Vehicles joins the Autonomous Vehicles Research team to conduct ground-breaking, publishable research on AI models for autonomous driving. You will develop large-scale supervised and reinforcement learning training frameworks for multi-modal foundation models, including agent behavior models and end-to-end architectures. Daily responsibilities include optimizing GPU and cluster utilization for training on massive datasets, implementing scalable data loaders for video and sensor data, and building GPU-accelerated simulation infrastructure. You will utilize PyTorch, JAX, or TensorFlow, alongside CUDA, Python, and C++. Key technical requirements include expertise in PPO, SAC, or Q-learning, and experience with Kubernetes and SLURM. This role solves complex problems in sim-to-real transfer, AI safety, and integrating LLMs with policy learning for real-world vehicle deployment.

Skills

PyTorch JAX TensorFlow Python C++ CUDA Kubernetes SLURM Reinforcement Learning Deep Learning MLOps Distributed Training LLMs Sim-to-Real HPC Natural Language Processing Graphics

What you'll do

Develop large-scale supervised and reinforcement learning training frameworks for multi-modal AV foundation models.
Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
Implement scalable data loaders and preprocessors for multimodal datasets including video, text, and sensor data.
Build and optimize GPU-accelerated simulation infrastructure to train driving policies at scale.
Develop sim-to-real transfer pipelines to deploy models from simulation to real-world cars.
Apply reinforcement learning to fine-tune multimodal large language models.
Develop robust monitoring and debugging tools for training workflows on large GPU clusters.

What we're looking for

Bachelor's degree in Computer Science, Robotics, Engineering, or a related field or equivalent experience.
10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.
Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
Deep familiarity with reinforcement learning algorithms like PPO, SAC, or Q-learning, including hyperparameter tuning and reward functions.
Familiarity with policy learning techniques like reward shaping, domain randomization, and curriculum learning.
Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
Strong programming skills in Python and a high-performance language such as C++.
Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools like SLURM.

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