Senior Performance Engineer - Deep Learning

Reposted Yesterday
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Santa Clara, CA, USA
In-Office
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Optimize and implement deep learning model kernels and libraries (e.g., Transformer Engine) for efficient scaling on NVIDIA GPUs. Collaborate on systems research, benchmark and submit results to community standards (MLPerf), engage with open-source and enterprise partners, and influence hardware and platform design.
Summary Generated by Built In

Our Deep Learning models performance engineering team at NVIDIA is hiring software engineers at all experience levels to build and optimize the libraries and tools that enable Deep Learning Researchers and Engineers to design, develop, and deploy efficient AI applications. We are an ambitious and diverse team that builds optimizations directly into mainstream open source Deep Learning frameworks - PyTorch and JAX, which boost the performance at all levels of NVIDIA's AI stack. Our team has a wide collaborative footprint, working not only with multiple teams across NVIDIA but also with the broader open-source community to deliver SOTA Deep Learning performance on the best AI platform in the world!

What you will be doing:

  • Build and support Transformer Engine, the open-source library for accelerating the training of Large Language Models.

  • Collaborate on systems research that improves Deep Learning model performance, such as training using extremely low precision, parallelism methods, etc.

  • Implement, benchmark, and optimize new Deep Learning models such as LLMs straight out of groundbreaking research to scale efficiently on NVIDIA GPUs and systems.

  • Build and contribute to NVIDIA submissions on community benchmarks such as MLPerf.

  • Engage with the open-source community as well as support enterprise customers and partners by delivering the benefits of NVIDIA’s latest hardware and software innovations.

  • Influence the design of new hardware generations and core platform software components for NVIDIA hardware and systems.

What we need to see:

  • BS or equivalent experience in Computer Science, Electrical Engineering, or a related field.

  • 3+ years of experience in C++ and Python programming.

  • Strong background, experience, or coursework in parallel systems programming, preferably on GPUs.

  • Knowledge of Computer Architecture, Code Optimization, and/or Operating Systems.

  • Proven experience in developing large software projects.

  • Excellent verbal and written communication skills.

Ways to stand out from the crowd:

  • Experience in PyTorch, JAX, or any other DL framework.

  • Experience with performance analysis, profiling, and code optimization techniques, especially with multi-GPU or multi-node systems.

  • Knowledge of modern LLM architectures, attention mechanisms, and/or low-level DL libraries such as cuBLAS, cuDNN, and cuSOLVER.

  • Experience in writing GPU kernels using any of - CUDA, OpenAI Triton, CuTeDSL, Pallas, or other similar libraries.

  • Any past contributions to the open source community and/or experience working with multidisciplinary teams also showcase readiness for the team's responsibilities. 

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 9, 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.

Skills Required

  • BS or equivalent in Computer Science, Electrical Engineering, or related field
  • 3+ years of experience in C++ and Python programming
  • Strong background, experience, or coursework in parallel systems programming (preferably on GPUs)
  • Knowledge of computer architecture, code optimization, and/or operating systems
  • Proven experience in developing large software projects
  • Excellent verbal and written communication skills
  • Experience with PyTorch, JAX, or other deep learning frameworks
  • Experience in performance analysis, profiling, and code optimization for multi-GPU or multi-node systems
  • Knowledge of modern LLM architectures, attention mechanisms, and low-level DL libraries (cuBLAS, cuDNN, cuSOLVER)
  • Experience writing GPU kernels using CUDA, OpenAI Triton, CuTeDSL, Pallas, or similar
  • Contributions to open-source projects or experience working with multidisciplinary teams

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 — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
  • Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
  • Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

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.”

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