Senior GPU Supercomputer Scheduler Engineer

NVIDIA is hiring a Senior GPU Supercomputer Scheduler Engineer in Santa Clara, United States. It pays $152k-$242k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include PyTorch, TensorFlow and AI research.
Level classifies it as a senior role.
Level lists 2,700+ other open roles at NVIDIA.

AI in this role

pytorchtensorflow

ai-research

NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Research Superclusters (MARS), builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you’ll help design solutions that power some of the world’s most advanced computing workloads.

As a member of the Scheduling team, you will participate in the design and implementation of groundbreaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. We seek engineers with deep technical expertise to identify architectural directions and new approaches for AI workload scheduling to serve many simultaneous and large multi-node GPU workloads with complex requirements and dependencies. This role offers you an excellent opportunity to deliver production grade solutions, get hands on with ground-breaking technology, and work closely with technical leaders solving some of the biggest challenges in machine learning, cloud computing, and system co-design.

What you'll be doing:

Design and develop new scheduling features and add-on services to improve GPU compute clusters across many dimensions, such as resource usage fairness, GPU occupancy, GPU waste, application resilience, application performance and power usage.
Design and develop batch workload management and orchestration services
Provide support to staff and end users to resolve batch scheduler issues
Build and improve our ecosystem around GPU-accelerated computing
Performance analysis and optimizations of deep learning workflows
Develop large scale automation solutions
Root cause analysis and suggest corrective action for problems large and small scales
Finding and fixing problems before they occur

What we need to see:

Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience
5+ years of work experience
Strong understanding of batch scheduling, preferably with experience in schedulers such as SLURM or K8s batch schedulers (Kueue, Volcano, etc.)
Significant experience in systems programming languages such as C/C++ & Go as well as scripting languages such as Python and bash
Established experience in Linux operating system, environment and tools
Experience analyzing and tuning performance for a variety of AI workloads
In-depth understating of container technologies like Docker, Singularity, Podman
Flexibility/adaptability for working in a dynamic environment with different frameworks and requirements
Excellent communication, interpersonal and customer collaboration skills

Ways to stand out from the crowd:

Knowledge in High-performance computing
Open Source Software Contribution
Experience with deep learning frameworks like PyTorch and TensorFlow
Passionate about SW development processes

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.

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 GPU Supercomputer Scheduler Engineer at NVIDIA rates 84 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 GPU Supercomputer Scheduler Engineer is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $152k-$242k a year.
The role is based in US, CA, Santa Clara.
The job was posted on August 13, 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

AI ResearchPyTorchTensorFlow

Questions you could be asked

Tell me about a research question you investigated. What did you find?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Research, PyTorch, and TensorFlow. 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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