We are now looking for a Senior AI Training Performance Engineer!
NVIDIA is seeking senior engineers who are obsessed with performance analysis and optimization to help us squeeze every last clock cycle out of AI training, one of the most important workloads in the world. If you are unafraid to work across all layers of the hardware/software stack from GPU architecture to Deep Learning Framework to achieve peak performance, we want to hear from you! This role offers the opportunity to directly impact the hardware and software roadmap in a fast-growing technology company that leads the AI revolution while helping deep learning users around the globe enjoy ever-higher training speeds.
What you will be doing:
Understand, analyze, profile, and optimize AI and deep learning training workloads on state-of-the-art hardware and software platforms.
Understand the big picture of training performance on GPUs, prioritizing and then solving problems across many dozens of state-of-the-art neural networks.
Implement production-quality software in multiple layers of NVIDIA's deep learning platform stack, from drivers to DL frameworks.
Implement key DL training workloads in NVIDIA's proprietary processor and system simulators to enable future architecture studies.
Build tools to automate workload analysis, workload optimization, and other critical workflows.
What we want to see:
PhD (or equivalent experience) in CS, EE or CSEE and 5+ years; or MS and 8+ years of relevant work experience.
Strong background in deep learning and neural networks, in particular training.
Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture.
Proven experience analyzing and tuning application performance.
Experience with processor and system-level performance modelling.
Programming skills in C++, Python, and CUDA.
Fluency in English
NVIDIA is increasingly known as the AI Computing company and is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. Are you passionate about performance? Are you interested in working on industry-leading Deep Learning products? Come, join our Deep Learning Architecture team, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly growing field.
Skills Required
- PhD in CS, EE or CSEE with 5+ years, or MS with 8+ years of relevant experience.
- Strong background in deep learning and neural networks, particularly training.
- Deep understanding of computer architecture and familiarity with GPU architecture.
- Proven experience analyzing and tuning application performance.
- Experience with processor and system-level performance modelling.
- Programming skills in C++, Python, and CUDA.
- Fluency in English.
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.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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.”
