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Software Engineer Gpu Performance Modeling Jobs

Waymo

Software Engineer, GPU

Mountain View, NY · On-site

To achieve our mission, we architect and create high-performance custom silicon; we develop system ... Expertise in C++ programming for GPU (CUDA or similar framework) * Bachelor degrees in EECS ...

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How much do software engineer gpu performance modeling jobs pay per year?

As of Sep 11, 2026, the average yearly pay for software engineer gpu performance modeling in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Software Engineer Gpu Performance Modeling job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

Senior System Software Engineer - GPU Performance

Santa Clara, CA • On-site

NVIDIA Corporation
Computer and Electronic Product Manufacturing • 10K+ employees
Learn more about NVIDIA Corporationexternal

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Re-posted 22 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

Job description

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.
We are the GPU Communications Libraries and Networking team at NVIDIA. We deliver libraries like NCCL, NVSHMEM, UCX for Deep Learning and HPC. We are looking for a motivated Performance engineer to influence the roadmap of our communication libraries. The DL and HPC applications of today have a huge compute demand and run on scales which go up to tens of thousands of GPUs. The GPUs are connected with high-speed interconnects (eg. NVLink, PCIe) within a node and with high-speed networking (eg. Infiniband, Ethernet) across the nodes. Communication performance between the GPUs has a direct impact on the end-to-end application performance; and the stakes are even higher at huge scales! This is an outstanding opportunity for someone with HPC and performance background to advance the state of the art in this space. Are you ready for to contribute to the development of innovative technologies and help realize NVIDIA's vision?
What you will be doing:
  • Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
  • Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack
  • Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available
  • Triage and root-cause performance issues reported by our customers
  • Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information
  • Collaborate with a very dynamic team across multiple time zones
What we need to see:
  • M.S. (or equivalent experience) or PhD in Computer Science, or related field with relevant performance engineering and HPC experience
  • 3+ yrs of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM)
  • Experience conducting performance benchmarking and triage on large scale HPC clusters
  • Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
  • Implement micro-benchmarks in C/C++, read and modify the code base when required
  • Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python
  • Familiar with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker)
  • Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones
Ways to stand out from the crowd:
  • Practical experience with Infiniband/Ethernet networks in areas like RDMA, topologies, congestion control
  • Experience debugging network issues in large scale deployments
  • Familiarity with CUDA programming and/or GPUs
  • Experience with Deep Learning Frameworks such PyTorch, TensorFlow
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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 September 13, 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.

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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