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What you'll be doing:
Benchmark and analyze AI workloads in single and multi-node configurations.
High level simulator and debugger development in C++/Python.
Evaluate PPA (performance, power, area) for hardware features and system-level architectural trade-offs.
Work closely with wider architecture teams, architecture and product management to help with trade-off analysis at every stage of the project.
Keep abreast with emerging trends and research in deep learning.
What we need to see:
MS or PhD in a relevant discipline (CS, EE, Math).
2+ years of experience in parallel computing architectures, interconnect fabrics and deep learning applications.
Strong programming skills in C, C++ and Python.
Proficiency in architecture analysis and performance modeling.
Curious mindset with excellent problem solving skills.
Ways to stand out from the crowd:
Understanding of modern transformer-based model architectures.
Experience with benchmarking, projections methodologies, workload profiling and correlation.
Ability to simplify and communicate rich technical concepts with non-technical audience.
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