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. As an NVIDIAN, you’ll be immersed in an inclusive, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
Join us at NVIDIA and be part of a team that’s crafting the future of technology! Our dedication to innovation and excellence has made us a leader in computer graphics, PC gaming, and accelerated computing for over 25 years. As we delve into the limitless opportunities that AI brings, we are looking for passionate individuals to help us push boundaries and redefine what's possible. NVIDIA is seeking a dedicated Software Engineer, ML Performance to join our team in Santa Clara, CA. In this role, you will engage directly with the latest AI technologies and work on optimizing performance at an outstanding scale. Your expertise will ensure that our groundbreaking solutions operate flawlessly and continue to lead the industry.
What you’ll be doing:
- Analyze the performance of distributed training and inference workloads throughout the entire stack: model and framework, data loading and checkpointing, compiler and kernels, CUDA and NCCL, host and network. Identify the bottleneck, resolve it or suggest a solution, and provide the supporting evidence.
- Define the performance metrics tracked across a fleet of GPUs. Break them down into factors engineers need to debug. Decide which signals the platform collects first.
- Drive the analysis of regressions: segment workloads across machines, versions, and configurations to isolate the cause, and turn the method into analysis others can reuse.
- Evaluate automated diagnoses: judge whether a finding is accurate, identify misleading results and missed problems, and build the labeled cases that keep the analysis honest.
- Work closely with build partners on their real workloads, carry findings through to improvements, and feed what the product is missing back into the roadmap.
What we need to see:
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
- 12+ years of relevant software engineering experience, including hands-on optimization of GPU training or inference workloads in production.
- Demonstrated performance work across more than one layer of the stack, with a clear account of the baseline, evidence, alternative explanations and validated outcome.
- Experience diagnosing performance across many jobs, machines or tenants, not only tuning a single workload.
- Enough familiarity with framework, runtime and compiler interfaces to say where a signal should be measured, even when someone else writes the instrumentation.
- Ability to distinguish symptoms from causes, recognize noisy or incomplete measurements and the effects of the measurement itself, and know when the evidence is insufficient.
- Strong programming and debugging skills, demonstrated by maintainable analysis code, tests or repeatable investigation procedures, including when using automated development tools.
- Clear communication of findings, trade-offs and remaining uncertainty to the people who own the workload.
- Proficiency with Python and CUDA.
Ways to stand out from the crowd:
- Improved operation of both training and inference.
- Deep knowledge of one or more of PyTorch, JAX, Megatron, NCCL, TensorRT-LLM, vLLM, SGLang or Dynamo.
- Experience moving between application, system software, driver or hardware roles, and the full-stack view that comes with it.
- Statistical judgment applied to noisy, multi-node fleet-scale performance data. Ideally, experience with counter-factual analysis and segmentation of workloads for diagnosing regressions.
- Built debugging or performance-analysis tools used by other engineers, with evidence that they made investigations more effective.
You will also be eligible for equity and benefits.
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.Skills Required
- Bachelor's degree in Computer Science, Computer Engineering, or a related field, or equivalent experience
- 12+ years of relevant software engineering experience
- Hands-on optimization of GPU training or inference workloads in production
- Performance work across more than one layer of the software stack
- Experience diagnosing performance across many jobs, machines, or tenants
- Familiarity with framework, runtime, and compiler interfaces
- Ability to distinguish symptoms from causes and evaluate noisy or incomplete measurements
- Strong programming and debugging skills
- Clear communication of findings, trade-offs, and uncertainty
- Proficiency with Python and CUDA
- Experience improving both training and inference
- Deep knowledge of one or more of PyTorch, JAX, Megatron, NCCL, TensorRT-LLM, vLLM, SGLang, or Dynamo
- Experience across application, system software, driver, or hardware roles
- Statistical judgment applied to noisy, multi-node fleet-scale performance data
- Experience with counterfactual analysis and workload segmentation for regression diagnosis
- Experience building debugging or performance-analysis tools used by other engineers
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
-
Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
-
Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
