AI and ML Performance Engineer   

We are now looking for an AI/ML Performance Engineer! At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking AI/ML Performance Engineer to contribute to the development of next-generation inference optimizations and deliver industry-leading performance. In this role, you will investigate and prototype scalable inference strategies—driving down per-token latency and maximizing system throughput by applying cross-stack optimizations that span algorithmic innovations (e.g., attention variants, speculative decoding, inference-time scaling), system-level techniques (e.g., model sharding, pipelining, communication overlap), and hardware-level enhancements.

As NVIDIA makes significant strides in AI datacenters, our team holds a central role in enhancing the efficiency of our exponentially growing inference deployment needs and establishing a data-driven approach to algorithmic improvements, hardware design, and system software development. We collaborate extensively with teams across deep learning research, framework development, compiler and systems engineering, and silicon architecture. Thriving in this high-impact, interdisciplinary environment demands not only technical proficiency but also a growth mindset and a pragmatic attitude—qualities that fuel our collective success in shaping the future of datacenter technology.

What You’ll Be Doing:

• Develop high-fidelity performance models to prototype emerging algorithmic techniques in Generative AI to drive model-hardware co-design.

• Design targeted optimizations for inference deployment to maximize Pareto frontier of Accuracy, Throughput and Interactivity.

• Quantify performance benefit of targeted optimizations to prioritize features and guide future software and hardware roadmap.

• Model end-to-end performance impact of emerging GenAI workflows - such as Agentic Pipelines, Inference-time compute scaling, etc. - to guide datacenter design and optimization.

• This position requires you to keep up with the latest DL research and collaborate with diverse teams, including DL researchers, hardware architects, and software engineers.

What we need to see:

• A minimum qualification of a Master's degree (or equivalent experience) in Computer Science, Electrical Engineering or related fields.

• Strong background in computer architecture, roofline modeling, queuing theory and statistical performance analysis techniques.

• Solid understanding of LLM internals (attention mechanisms, FFN structures), model parallelism and inference serving techniques.

• 3+ years of hands-on experience in system evaluation of AI/ML workloads or performance analysis, modeling and optimizations for AI.

• Proficiency in Python (and optionally C++) for simulator design and data analysis.

• Growth mindset and pragmatic “measure, iterate, deliver” approach.

Ways to Stand Out from the Crowd:

• Comfortable defining metrics, designing experiments and visualizing large performance datasets to identify resource bottlenecks.

• Proven track record of working in cross-functional teams, spanning algorithms, software and hardware architecture.

• Ability to distill complex analyses into clear recommendations for both technical and non-technical stakeholders.

• Experience with GPU computing (CUDA)

• Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang

NVIDIA 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 in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you!

The base salary range is 148,000 USD - 287,500 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.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.