Digital Verification Engineer

Palo Alto, California
Engineering/Research
Full Time
Fathom Computing is developing high-performance machine learning computers built to run both training and inference workflows for large-scale artificial neural networks (ANNs).  As computing is largely limited by data transfer Fathom’s approach combines the power of CMOS electronics with advantages of optical data movement for performance far beyond what electronics-only computers are capable of.

We’re seeking a digital verification engineer with a strong first principles approach to IC design and verification with an interest in neural networks and ML hardware. You will collaborate closely with colleagues from other disciplines to lead the verification effort for our ML accelerator.
 
Your primary scope of ownership includes verification planning, verification environment development, simulation, and data analysis.  You should also be comfortable contributing to ASIC design.

Areas of contribution

    • Plan, document, and implement ASIC verification strategy including determining appropriate toolset, methodologies, metrics, and coverage
    • Develop simulation platform and automate collection of verification metrics
    • Along with other designers, determine the appropriate course of action for correcting bugs found through the verification platform

Requirements

    • MS/PhD in Computer Engineering, Electrical Engineering, or Computer Science
    • Wide range of experience in digital ASIC design
    • Proficiency in Cadence or Synopsys simulation and modeling tools
    • Hands-on experience in prototype bring-up, debugging, and functional verification
    • Thorough understanding of UVM in SystemVerilog
    • Ability to perform comprehensive electronic system characterization
    • Strong project management, planning, and organizational skills
    • Excellent communication skills and ability to collaborate on complex cross-disciplinary systems
    • Drive to build something that hasn’t been built before

Preferred

    • Significant academic or work contributions (including but not limited to published papers, patents, industry, and personal projects)
    • Deep understanding of the underlying physics in current silicon electronics
    • Knowledge or experience in machine learning and computer architecture

You'll do well here if…

    • You enjoy thoughtful discussions fueled by problem-solving and logic
    • You're comfortable both leading and contributing individually
    • You're excited about the future of ML hardware
    • You enjoy teaching and learning from an interdisciplinary team
We highly encourage submission of a cover letter, just tell us why you're here :)