AI Hardware Architecture

Responsibilities

• We are seeking exceptional individuals who will help to propose, design, and evaluate analog-computing hardware architectures for achieving a 1000x energy-efficiency advantage in ML inference.

Requirements

• Exceptional technical training and ability in a quantitative field (e.g., Physics, Electrical Engineering, Computer Science, or a related discipline): An MS/PhD or equivalent research/project experience is strongly preferred.
• Deep knowledge of and experience in at least one of: AI hardware architecture design or integrated-circuit design (the latter need not relate directly to AI).
• Analog and/or other unconventional physics-based computing: Exposure to and familiarity with the basic principles and engineering practice of these paradigms.
• Modern generative AI models: Familiarity with how modern (post-2017) AI models work and what this might imply for analog computer systems design.
• Full-stack Communication: Excellent ability to communicate complex technical concepts to diverse teams across the full stack from algorithms to circuits.

Qualifications

• The ideal candidate will have a deep understanding of the principles and engineering practice of analog and/or other unconventional physics-based computing paradigms and a proven ability to design and evaluate analog/unconventional computing hardware architectures.
• Energy modeling and optimization: Experience modeling the energy consumption of given architectural designs for AI workloads, and optimizing architectures for minimal energy consumption while maintaining ML performance.
• Hardware-algorithm co-design: Experience designing systems where both the AI algorithm/model and the hardware are optimized simultaneously - as opposed to architecting a system where the system behavior is specified a priori based on the needs of algorithms as they are run on GPUs.
• Modern generative AI models: Experience with designing hardware to run modern generative AI models.
• Non-volatile memories: Experience with detailed systems design and evaluation using at least one type of emerging non-volatile memory (e.g., RRAM), and knowledge of the tradeoffs of other non-volatile memories.

Skills

• Exceptional technical training and ability in a quantitative field (e.g., Physics, Electrical Engineering, Computer Science, or a related discipline)
• Deep knowledge of and experience in at least one of: AI hardware architecture design or integrated-circuit design (the latter need not relate directly to AI)
• Analog and/or other unconventional physics-based computing
• Modern generative AI models
• Full-stack Communication
• Energy modeling and optimization
• Hardware-algorithm co-design
• Non-volatile memories

Benefits

• A comprehensive package including best-in-class health benefits
• 401k matching
• truly unlimited PTO
• complimentary meals in our Palo Alto office

Pay

• Competitive compensation package

Schedule

• Full-time position