Senior Performance Engineer - Deep Learning

About the role

The Deep Learning models performance engineering team at NVIDIA is seeking software engineers at various levels to contribute to the development and optimization of libraries and tools that facilitate the creation and deployment of efficient AI applications. Our team works closely with researchers and engineers to enhance the performance of deep learning models across NVIDIA's AI stack.

Responsibilities

• Build and support Transformer Engine, an open-source library designed to accelerate the training of large language models.
• Collaborate on systems research aimed at improving deep learning model performance through techniques like training with extremely low precision and parallelism methods.
• Implement, benchmark, and optimize new deep learning models, including large language models, to ensure they run efficiently on NVIDIA GPUs and systems.
• Contribute to NVIDIA's submissions on community benchmarks such as MLPerf.
• Engage with the open-source community and support enterprise customers and partners by leveraging NVIDIA's latest hardware and software innovations.
• Influence the design of new hardware generations and core platform software components for NVIDIA hardware and systems.

Requirements

• Bachelor's degree 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, particularly on GPUs.
• Knowledge of computer architecture, code optimization, and operating systems.
• Proven experience in developing large software projects.
• Excellent verbal and written communication skills.

Qualifications

• Experience in PyTorch, JAX, or other deep learning frameworks.
• Experience with performance analysis, profiling, and code optimization techniques, especially with multi-GPU or multi-node systems.
• Knowledge of modern large language model architectures, attention mechanisms, and low-level deep learning libraries such as cuBLAS, cuDNN, and cuSOLVER.
• Experience writing GPU kernels using CUDA, OpenAI Triton, CuTeDSL, Pallas, or similar libraries.
• Past contributions to the open-source community and/or experience working with multidisciplinary teams.

Skills

• Experience with performance analysis, profiling, and code optimization techniques, especially with multi-GPU or multi-node systems.
• Knowledge of modern large language model architectures, attention mechanisms, and low-level deep learning libraries such as cuBLAS, cuDNN, and cuSOLVER.
• Experience writing GPU kernels using CUDA, OpenAI Triton, CuTeDSL, Pallas, or similar libraries.
• Past contributions to the open-source community and/or experience working with multidisciplinary teams.

Benefits

Base salary is determined based on location, experience, and the pay of employees in similar positions. The base salary range is $152,000 - $241,500 for Level 3 and $184,000 - $287,500 for Level 4. Eligible for equity and benefits.

Pay

Base salary range: $152,000 - $241,500 for Level 3, and $184,000 - $287,500 for Level 4.

Schedule

Not specified.

Benefits

Not specified.

Skills

• Experience with performance analysis, profiling, and code optimization techniques, especially with multi-GPU or multi-node systems.
• Knowledge of modern large language model architectures, attention mechanisms, and low-level deep learning libraries such as cuBLAS, cuDNN, and cuSOLVER.
• Experience writing GPU kernels using CUDA, OpenAI Triton, CuTeDSL, Pallas, or similar libraries.
• Past contributions to the open-source community and/or experience working with multidisciplinary teams.

Benefits

Not specified.

Application Instructions

Applications for this job will be accepted at least until March 8, 2026.

Contact Information

NVIDIA is committed to fostering a diverse 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.