Senior Research Engineer, Foundation Model Training Infrastructure

NVIDIA is hiring a Senior Research Engineer, Foundation Model Training Infrastructure in Santa Clara, United States. It pays $224k-$357k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
Tools and skills the posting cites include PyTorch, TensorFlow and JAX.
Level classifies it as a senior role.
Level lists 2,600+ other open roles at NVIDIA.

AI in this role

pytorchtensorflowjax

fine-tuningml-ops

NVIDIA is searching for a senior or principal engineer who specializes in building cutting-edge infrastructure for large-scale foundation model training in the Generalist Embodied Agent Research (GEAR) group. Our team is leading Project GR00T, NVIDIA’s moonshot initiative at building foundation models and full-stack technology for humanoid robots.

You will work with an amazing and collaborative research team that consistently produces influential works on multimodal foundation models, large-scale robot learning, embodied AI, and physics simulation. Our past projects include Eureka, VIMA, Voyager, MineDojo, MimicPlay, Prismer, and more. Your contributions will have a significant impact on our research projects and product roadmaps.

What you will be doing:

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.

What we need to see:

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 with 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).

Ways to stand out from the crowd:

Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field;
Demonstrated Tech Lead experience, coordinating a team of engineers and driving projects from conception to deployment;
Strong experience at building large-scale LLM and multimodal LLM training infrastructure;
Contributions to popular open-source AI frameworks or research publications in top-tier AI conferences, such as NeurIPS, ICRA, ICLR, CoRL.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world. Please join us and be part of the forefront of developing general-purpose robots and large-scale foundation models!

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Senior Research Engineer, Foundation Model Training Infrastructure at NVIDIA rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Research Engineer, Foundation Model Training Infrastructure is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $224k-$357k a year.
The role is based in US, CA, Santa Clara.
The job was posted on January 9, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsPyTorchTensorFlowJAX

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
What are the limits of PyTorch that you've run into, and how did you work around them?
What's a project where you used TensorFlow hands-on?
Walk me through how you've used JAX in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Fine-tuning, ML Ops, PyTorch, TensorFlow, and JAX. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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