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Unconventional AI

Analog computing substrate for energy-efficient AI

AI Silicon - Design Verification

Hardware Validation & TestingConfirmed live in the last 24 hours
Full-timeMountain View, CA, USANo salary listedIn-person

About the Job

Requirements

  • A bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
  • At least 8 years of hands-on experience with advanced verification methodologies and languages, specifically Universal Verification Methodology (UVM) and SystemVerilog.
  • A proven track record of developing and maintaining complex verification testbenches and automated test environments.
  • Proficiency in scripting languages such as Python, Shell, or Bash for automating verification flows and analyzing results.
  • Strong familiarity with Linux and industry-standard tools from Cadence and Synopsys.

Responsibilities

  • Define, architect, and implement comprehensive verification plans and environments for IP blocks and full chip from specification through tape-out.
  • Help build verification infrastructure from the ground up, creating flexible and nimble environments that can move quickly and scale for growth.
  • Take full ownership of the verification lifecycle, including developing and maintaining scalable testbenches, sophisticated test cases, and robust verification environments.
  • Deploy industry-standard methodologies, including UVM and SystemVerilog, for constrained-random stimulus generation, coverage-driven verification, and assertions.
  • Collaborate with digital and analog design teams, as well as software and systems teams, to define verification requirements, debug complex hardware/software interactions, and ensure design integrity.
  • Perform exhaustive functional and code coverage analysis and debug failures at the RTL and gate level to achieve verification closure and high-quality silicon.

Desired Qualifications

  • A master’s degree or Ph.D. in Electrical Engineering or Computer Science, with an emphasis on computer architecture.
  • Expert-level knowledge of UVM architecture and high-level behavioral modeling.
  • Experience with modern AI-driven code generation tools.

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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