Hybrid Senior Deep Learning Performance Architect

Posted 2 days ago

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About the role

  • Performance Architect at NVIDIA developing innovative hardware architectures for AI and high-performance computing. Collaborating on performance modeling and system-level optimizations across large-scale deep learning workloads.

Responsibilities

  • Develop innovative HW architectures to extend the state of the art in parallel computing performance, energy efficiency and programmability.
  • Build the mathematical frameworks required to reason about system availability and workload goodput at massive scales.
  • Reason about overall Deep Learning workload performance under various scheduling, parallelization, and resiliency strategies.
  • Conduct "what-if" studies on hardware configurations, infrastructure knobs, and workload strategies to identify optimal system-level trade-offs.
  • Work closely with wider architecture and product teams to guide the hardware/software roadmap using data-driven performance and reliability projections.
  • Build and refine high-level simulators in python to model the interaction between knobs that impact performance and resiliency.

Requirements

  • MS or PhD in a Computer Science, Computer Engineering, Electrical Engineering or equivalent experience.
  • 6+ years of relevant industry or research work experience.
  • Strong background in analytical and probabilistic modeling.
  • 2+ years of experience in parallel computing architectures, distributed systems, or interconnect fabrics.
  • A strong understanding of distributed deep learning workloads scheduling in large scale systems.
  • Proficiency in Python for building performance and reliability models.

Benefits

  • equity
  • benefits

Job title

Senior Deep Learning Performance Architect

Job type

Experience level

Senior

Salary

$184,000 - $287,500 per year

Degree requirement

Postgraduate Degree

Location requirements

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