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Architect, analyse and optimize high-performance GPU-centric SoCs for Machine Learning workload. Develop performance models and methodologies. Propose solutions to enhance performance and optimize power for next-generation data centre systems.
Job Responsibility:
Define, build and maintain performance models for performance projections, analysis and architecture exploration
Develop and execute system-level modelling strategies for ML and GPU hardware and software co-design
Drive performance trade-off studies for new architectural features, algorithms, and system configurations, providing data-driven recommendations
Collaborate with architecture, design and software teams to integrate models, define workloads and analyse simulation results
Innovate and advance modelling methodologies, tools and infrastructure to improve accuracy, speed, and architectural insight
Requirements:
Ph.D. in Computer Science / Electronics Engineering, and 1+ years of experience as a Performance Engineer
M.S./M.Tech. in Computer Science / Electronics Engineering, and 3+ years of experience as a Performance Engineer
B.Tech. in Computer Science / Electronics Engineering, and 5+ years of experience as a Performance Engineer
Strong understanding of computer architecture
Experience of working in GPUs, SoCs, ML accelerators would be a plus
Exposure to performance analysis, workload characterization, and hardware/software co-design exploration
Familiarity with ML models and software stacks relevant to ML
Understanding of AI model distributed training and inference, model layers and ML ops, parallelization strategies
Understanding of system-level modelling and simulation will be a plus
Strong programming skills, including experience with Python (or similar)
Nice to have:
Experience of working in GPUs, SoCs, ML accelerators
Understanding of system-level modelling and simulation