Performance Engineer - Deep Learning

AI in this role

Build and optimize deep learning libraries and tools for accelerated AI applications and large language models on NVIDIA GPUs.

openaipytorchjaxcudatransformer-engine

deep-learningcpythonperformance-optimization

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.
2+ 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 the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 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.

How we rate this

Performance Engineer - Deep Learning at NVIDIA rates 90 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. Open the description and count.

Key facts

Performance Engineer - Deep Learning is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $124k-$196k a year.
The role is based in US, CA, Santa Clara.
The job was posted on October 5, 2026.
Seniority: mid.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Deep LearningCPythonPerformance OptimizationOpenAIPyTorchJaxCuda

Questions you could be asked

Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where c was part of your work. What did you do?
Tell me about a project where python was part of your work. What did you do?
Tell me about a project where performance optimization was part of your work. What did you do?
Walk me through how you've used OpenAI in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Deep Learning, C, Python, Performance Optimization, and OpenAI. 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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