AI and ML Performance Engineer

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.

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.
Keep up with the latest DL research and collaborate with diverse teams, including DL researchers, hardware architects, and software engineers.

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