Join NVIDIA’s Autonomous Vehicles Research team as a senior research engineer where you will develop large-scale supervised learning and reinforcement learning frameworks to support multi-modal foundation models for AVs. You’ll optimize GPU and cluster utilization, implement scalable data loaders, build simulation infrastructure, and collaborate with researchers to integrate cutting-edge model architectures into training pipelines. Additionally, you'll work on sim-to-real transfer pipelines and deploy solutions to real-world cars while applying reinforcement learning to fine-tune multimodal LLMs. Ideal candidates have over 10 years of industry experience in MLOps and AI infrastructure, expertise with PyTorch or TensorFlow, deep familiarity with reinforcement learning algorithms like PPO and SAC, and strong programming skills in Python and C++. Experience with CUDA programming and cluster management tools is essential for this role focused on advancing autonomous driving technology.
Develop large-scale supervised learning and reinforcement learning training frameworks for AVs.
Optimize GPU and cluster utilization for efficient model training on massive datasets.
Implement scalable data loaders tailored for multimodal datasets including videos, text, and sensor data.
Build simulation infrastructure to support the training of driving policies for AVs at scale.
Develop sim-to-real transfer pipelines for deploying models to real-world cars.
Propose scalable solutions combining LLMs with policy learning techniques.
Apply reinforcement learning to fine-tune multimodal large language models.
What we're looking for
10+ years of industry experience in large-scale MLOps and AI infrastructure.
Proven expertise in designing and optimizing distributed training systems using PyTorch, JAX, or TensorFlow.
Deep knowledge of reinforcement learning algorithms including PPO, SAC, Q-learning, and reward shaping techniques.
Proficiency in GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
Strong programming skills in Python and C++ for efficient system development.
Experience with large-scale GPU clusters, HPC environments, and job scheduling tools such as SLURM or Kubernetes.
Market check
Salary context
Competitive pay
How this pay compares to similar roles
Similar $206k
This role $236k
$146k
most similar roles pay here
$303k
This role pays more than
62%
of similar roles. Most pay
$161,575–$249,753
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$206k
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
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Listed pay typically runs
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across 812 roles with salary data.