Senior Performance Engineer, Deep Learning

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Posted: 28 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $211k

This role $197k

$139k most similar roles pay here $276k

This role pays less than 63% of similar roles. Most pay $176,000–$246,150 — the shaded band above. At the midpoint, this role pays about $197k versus about $211k for comparable roles.

Based on 240 similar postings.

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Performance Engineer, Deep Learning

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As a Senior Performance Engineer - Deep Learning, you will join the Deep Learning models performance engineering team to build and optimize libraries and tools that enable researchers to develop efficient AI applications. You will focus on building and supporting Transformer Engine for accelerating Large Language Model training while collaborating on systems research involving low precision and parallelism methods. Your daily work involves implementing, benchmarking, and optimizing new models like LLMs to scale on GPUs, contributing to community benchmarks like MLPerf, and influencing hardware design. The role requires proficiency in C++, Python, and parallel systems programming, with knowledge of Computer Architecture and Operating Systems. You will utilize frameworks such as PyTorch and JAX, along with libraries like cuBLAS, cuDNN, and cuSOLVER, while potentially utilizing CUDA or OpenAI Triton to develop GPU kernels for high-performance computing.

Skills

C++ Python PyTorch JAX CUDA OpenAI Triton cuBLAS cuDNN cuSOLVER Parallel Systems Code Optimization LLM Transformer Engine MLPerf Computer Architecture Operating Systems Multi-node Systems

What you'll do

Build and support the Transformer Engine library to accelerate Large Language Model training.
Implement and optimize new Deep Learning models to scale efficiently on NVIDIA GPUs and systems.
Conduct systems research to improve performance through low precision training and parallelism methods.
Benchmark and optimize code for community benchmarks such as MLPerf.
Contribute to open-source projects including PyTorch and JAX frameworks.
Engage with the open-source community and enterprise customers to deliver hardware and software innovations.
Influence the design of new hardware generations and core platform software components.

What we're looking for

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 or experience 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 with performance analysis, profiling, and code optimization for multi-GPU or multi-node systems.

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