Senior Autonomous AI Research Engineer — Large-Scale ML

NVIDIA Gruppe

Santa Clara (CA)

On-site

USD 224,000 - 357,000

Full time

14 days+

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Job summary

NVIDIA in California's Santa Clara area is seeking senior research engineers for the Autonomous Vehicles Research team. You will conduct ground-breaking publishable research while shaping AI for AV products across CUDA, simulation, and robotics.

The role requires deep learning, RL, and multimodal modeling expertise, with opportunities to deploy to real-world cars and collaborate with product teams. Applicants should have 10+ years of experience, strong Python/C++, and CUDA proficiency; equity is

Qualifications

Bachelor's degree in Computer Science, Robotics, Engineering, or 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 experience tuning hyperparameters and reward functions.
Familiarity with policy learning techniques like reward shaping, domain randomization, 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++ for efficient system development.
Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes).

Responsibilities

Develop large-scale supervised learning and reinforcement learning training frameworks to support multi-modal foundation models for AVs capable of running on thousands of GPUs.
Optimize GPU and cluster utilization for efficient model training and fine‑tuning on massive datasets.
Implement scalable data loaders and preprocessors tailored for multimodal datasets, including videos, text, and sensor data.
Build and optimize simulation infrastructure (based on GPU‑accelerated simulators) to support the training of driving policies for AVs at scale.
Collaborate with researchers to integrate cutting‑edge model architectures into scalable training pipelines.
Develop sim‑to‑real transfer pipelines and work closely with the AV product team to deploy to real‑world cars.
Propose scalable solutions that combine LLMs with policy learning.
Apply reinforcement learning to finetune multimodal LLMs.
Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters.