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Unconventional AI

Analog computing substrate for energy-efficient AI

AI Silicon - Analog Design

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

About the Job

Requirements

  • A Master's degree in Electrical Engineering or a Ph.D. in Electrical Engineering is required.
  • At least 5 years of hands-on, full-cycle analog/mixed-signal integrated circuit design experience, including successfully taped-out and characterized silicon.
  • Deep understanding of transistor-level circuit design, device physics, and noise/mismatch mechanisms in CMOS processes.
  • Proficiency with scripting languages such as Python or MATLAB for design-flow automation and data post-processing.

Responsibilities

  • Define, architect, and implement complex analog mixed-signal circuits from specification to GDSII in deep sub-micron CMOS technologies.
  • Own the full design cycle for critical IP blocks.
  • Conduct comprehensive pre-layout and post-layout verification, including analysis of PVT variations, noise, mismatch using Monte Carlo, stability, and reliability including EM/IR.
  • Collaborate with digital design teams to define interfaces, conduct top-level mixed-signal simulation, and ensure seamless integration of analog and digital blocks.
  • Define detailed floorplans and critical layout guidelines for high-performance blocks, and work with layout engineers to optimize parasitics, matching, and signal integrity.

Desired Qualifications

  • Experience designing circuits in modern process nodes such as 22nm, 12nm, and newer.
  • Deep understanding of device physics.
  • Experience with behavioral modeling of analog circuits and impairments for system-level verification using tools or languages such as Verilog, Python, C++, or MATLAB.

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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