Senior Research Engineer, Foundation Model Training Infrastructure at NVIDIA

Santa Clara, California, United States

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$224,000 – $356,500Compensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial Intelligence, RoboticsIndustries

Requirements

Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field
10+ years of full-time industry experience in large-scale MLOps and AI infrastructure
Proven experience designing and optimizing distributed training systems using frameworks like PyTorch, JAX, or TensorFlow
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

Design and maintain large-scale distributed training systems to support multi-modal foundation models for robotics
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
Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters
Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines

Skills

Key technologies and capabilities for this role

PyTorchJAXTensorFlowCUDAKubernetesPythonC++SLURMMLOpsGPU accelerationdistributed training

Questions & Answers

Common questions about this position

What is the salary range for this Senior Research Engineer position?

The base salary range is $224,000 - $356,500 USD.

Is this role remote or hybrid, and where is it located?

The position is hybrid.

What are the key skills and experience required for this role?

Candidates need 10+ years in large-scale MLOps and AI infrastructure, experience with PyTorch/JAX/TensorFlow, deep GPU/CUDA knowledge, Python/C++ programming, and large-scale GPU cluster experience.

What is the team and work environment like at NVIDIA for this role?

You'll collaborate with a talented and collaborative research team in the Generalist Embodied Agent Research (GEAR) group on Project GR00T, contributing to influential work in multimodal foundation models and humanoid robots. NVIDIA is widely considered one of the technology world’s most desirable employers.

What makes a candidate stand out for this Senior Research Engineer role?

Stand out with an advanced degree (Master’s or PhD), Tech Lead experience coordinating teams, experience building large-scale LLM/multimodal LLM training infrastructure, and contributions to open-source AI frameworks or top-tier publications like NeurIPS or ICML.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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