At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking AI/ML System 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 maximizing 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. Sample projects include Helix Parallelism and Disaggregated Inference. - Optimize inference deployment by pushing the Pareto frontier of Accuracy, Throughput and Interactivity at datacenter scale - Develop high-fidelity performance models to prototype emerging algorithmic techniques & hardware optimizations to drive model-hardware co-design for Generative AI. - Prioritize features to guide future software and hardware roadmap based on detailed performance modeling and analysis - Model end-to-end performance impact of emerging GenAI workflows - such as Agentic Pipelines, Inference-time compute scaling, etc. – to understand future datacenter needs - 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. - Pursuing or recently completed a MS or PhD 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 Machine Learning fundamentals, model parallelism and inference serving techniques. - Proficiency in Python (and optionally C++) for simulator design and data analysis. - Experience in system evaluation of AI/ML workloads or performance analysis, modeling and optimizations for AI. - 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) or 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 and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 120,000 USD - 189,750 USD for Level 2, and 148,000 USD - 235,750 USD for Level 3. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until August 24, 2025.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.