Performance Co-Design Engineer, Google Cloud TPU

• Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models.
  
• Collaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance.
  
• Lead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions.
  
• Conduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements.
• Engage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production.

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
• 10 years of experience in computer architecture, chip architecture, or hardware-software co-design.
• Experience developing systems for performance modeling, simulation, or system analysis.

Preferred qualifications:

• Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
• Experience architecting hardware solutions or performance optimizations for large-scale ML training and inference.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Deep understanding of ML trends, business drivers, and the software ecosystem.
• Ability to engage and collaborate with hardware designers, software architects, and ML researchers.

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
• 10 years of experience in computer architecture, chip architecture, or hardware-software co-design.
• Experience developing systems for performance modeling, simulation, or system analysis.