NVIDIA
Responsibilities
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. Requirements
A Master's degree (or equivalent experience) in Computer Science, Electrical Engineering or related fields. 3+ years of hands-on experience in system evaluation of AI/ML workloads or performance analysis, modeling and optimizations for AI Strong background in computer architecture, roofline modeling, queuing theory and statistical performance analysis techniques. Solid understanding of ML fundamentals, model parallelism and inference serving techniques. Proficiency in Python (and optionally C++) for simulator design and data analysis. Experience with GPU computing (CUDA) Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang Growth mindset and pragmatic “measure, iterate, deliver” approach. Preferred Qualifications
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. 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.
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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. Requirements
A Master's degree (or equivalent experience) in Computer Science, Electrical Engineering or related fields. 3+ years of hands-on experience in system evaluation of AI/ML workloads or performance analysis, modeling and optimizations for AI Strong background in computer architecture, roofline modeling, queuing theory and statistical performance analysis techniques. Solid understanding of ML fundamentals, model parallelism and inference serving techniques. Proficiency in Python (and optionally C++) for simulator design and data analysis. Experience with GPU computing (CUDA) Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang Growth mindset and pragmatic “measure, iterate, deliver” approach. Preferred Qualifications
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. 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.
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