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Unconventional AI

Analog computing substrate for energy-efficient AI

Junior Digital Design Engineer - AI Silicon

Silicon & ASIC DesignConfirmed 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 4 years of industry experience in digital ASIC/SoC design.
  • Expertise in Verilog RTL coding and digital design fundamentals.
  • Familiarity with standard EDA tools for simulation, assertions, synthesis, linting, and clock domain crossing analysis.
  • Understanding of the full ASIC design flow, from specification to tape-out.
  • Excellent written and verbal communication skills.

Responsibilities

  • Contribute to the micro-architecture definition and integration of digital blocks, including CPU, interfaces, and memory subsystems.
  • Assist in integrating third-party intellectual property into the main SoC fabric.
  • Write clean, synthesizable, high-performance Verilog/SystemVerilog code.
  • Perform hands-on RTL design for SoC blocks and digital interfaces, focusing on power, performance, and area targets.
  • Implement and debug key digital interfaces to ensure system stability.
  • Implement low-power design techniques, including clock gating and power domains.
  • Perform initial synthesis and formal verification (LEC) to ensure the design is routable and meets frequency targets.
  • Create block-level timing constraints.
  • Run and resolve RTL linting, clock domain crossing, and reset domain crossing checks using industry-standard tools.
  • Participate in the block-level verification plan and help develop a robust verification environment.
  • Triage and debug simulation failures to ensure functional correctness.
  • Support created technical interfaces and assist with hand-off between internal design and physical implementation teams.

Desired Qualifications

  • Experience with design constraints or architectures related to machine learning or neural network accelerators.
  • Experience with UVM environments for basic debugging.
  • Knowledge of formal verification techniques.
  • Exposure to post-silicon bring-up and lab debugging using logic analyzers or oscilloscopes.
  • Experience with modern design methods, including generative AI code development platforms.
  • Experience integrating large analog mixed-signal blocks into larger subsystems.

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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