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Unconventional AI

Analog computing substrate for energy-efficient AI

AI Hardware Architecture

Silicon & ASIC DesignConfirmed live in the last 24 hours
Full-timeMountain View, CA, USAIn-person

About the Job

Requirements

  • Exceptional technical training and ability in a quantitative field, such as 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; integrated-circuit design need not relate directly to AI.
  • Exposure to and familiarity with the basic principles and engineering practice of analog and/or other unconventional physics-based computing paradigms.
  • Familiarity with how modern (post-2017) AI models work and what this might imply for analog computer systems design.
  • Ability to communicate complex technical concepts to diverse teams across the full stack, from algorithms to circuits.

Responsibilities

  • Propose, design, and evaluate analog-computing hardware architectures for achieving a 1000x energy-efficiency advantage in machine-learning inference.
  • Drive invention, prototyping, and validation of core components of the novel computing platform.
  • Contribute across theoretical modeling and simulation, algorithm development, hardware/software co-design, or experimental validation in collaboration with other team members.
  • Help chart the technical roadmap.

Desired Qualifications

  • Deep understanding of the principles and engineering practice of analog and/or other unconventional physics-based computing paradigms, and demonstrated ability to design and evaluate analog/unconventional computing hardware architectures.
  • Experience modeling energy consumption for architectural designs serving AI workloads and optimizing architectures for minimal energy consumption while maintaining machine-learning performance.
  • Experience designing systems in which the AI algorithm/model and hardware are optimized simultaneously, rather than specifying system behavior a priori based on algorithms running on GPUs.
  • Experience designing hardware to run modern generative AI models.
  • Experience with detailed systems design and evaluation using at least one type of emerging non-volatile memory, such as RRAM, and knowledge of the tradeoffs of other non-volatile memories.

About the Company

Unconventional AI

Analog computing substrate for energy-efficient AI

Unconventional AI designs analog AI hardware and silicon substrates that run neural networks directly in hardware. Its chips use analog circuits that mimic biological neuron dynamics to process AI workloads in the physics of silicon, reducing energy use compared with digital GPUs. It differentiates itself by pursuing an in-hardware, neuromorphic-style substrate rather than optimizing existing GPUs, supported by prominent venture funding. Its goal is to counter the AI energy bottleneck and enable scalable AI in data centers with biology-scale efficiency.

Company size
51-200
Company stage
Seed
Total funding
$475M
Headquarters
San Diego, CA
Founded
2025

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