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Unconventional AI

Analog computing substrate for energy-efficient AI

AI Silicon Validation Engineer

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

About the Job

Requirements

  • A B.S. or M.S. in Electrical Engineering, Computer Engineering, or a related field.
  • At least 5 years of industry experience in post-silicon bring-up, hardware validation, or silicon characterization.
  • Familiarity with operating lab test equipment such as oscilloscopes, logic analyzers, and power analyzers.
  • Demonstrated proficiency in characterizing and debugging mixed-signal circuits.
  • Experience with Python programming and lab automation.
  • Excellent written and verbal communication skills.

Responsibilities

  • Lead the initial power-on and bring-up of new silicon prototypes and hardware platforms in the lab.
  • Evaluate and benchmark the functionality, performance, and power consumption of complex mixed-signal circuits to ensure alignment with design specifications and simulation models.
  • Use lab test equipment such as oscilloscopes, logic analyzers, and power analyzers to identify, triage, and solve anomalies across silicon, board, and software.
  • Design and maintain Python-based automation frameworks to accelerate characterization throughput, test repeatability, and data reporting.
  • Collaborate with the System Modeling and Analog and Digital Design teams to define validation requirements, interpret simulation-to-silicon correlation, and drive root-cause analysis.

Desired Qualifications

  • Experience evaluating the performance tradeoffs of current neural network architectures.
  • Experience correlating mixed-signal measurements on silicon to simulation models.
  • Understanding of hardware/software co-design concepts for machine learning inference or training.
  • Familiarity with FPGA emulation and RTL simulation to leverage pre-silicon verification for post-silicon bring-up.
  • Familiarity with AI automation.

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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