Digital IC Designer and Systems Engineer

  • Palo Alto, United States

Digital IC Designer and Systems Engineer

Job description

Fathom Computing is building hardware for the future of machine intelligence. Our optical computer will allow training of vast neural networks with unprecedented performance. We're also a Public Benefit Corporation looking to make the next generation of AI safe, humane, and beneficial.


We’re seeking a digital IC designer and electronic systems architect with strong principles-first understanding of physics and IC design, plus an interest in neural networks. You will collaborate closely with colleagues from other disciplines (optics, machine learning, etc.) to lead the design and implementation of Fathom computers.


Your scope of ownership includes: silicon architecture, IC design/implementation/validation, and working with SW to build accurate simulations. Examples of responsibilities are listed below.

  • Drive the design and implementation of the electronic aspects of a novel, complex, high-performance, deeply-integrated computing system
  • Architect silicon-based electronics
  • Contribute to the development and stewardship of all aspects of the system such as power budgets, thermal solutions, chip-level and system-level packaging schema
  • Select outside vendors and work with them to implement and fabricate custom ICs
  • Integrate Fathom’s custom IC into the larger opto-electronic system

Note: This is not an entry-level position.

Requirements

  • BS/MS/PhD in EE or physics.
  • Wide range of experience with focus on digital IC design (e.g. analog design, ADC/DAC, high speed digital, schematic capture and PCB layout, FPGA programming, ASIC design, CMOS or other image sensors, TSVs, stacked die and/or other esoteric semiconductor packaging, etc.)
  • Experience developing low volume, high performance systems utilizing cutting-edge technologies.
  • Proficiency in MATLAB, SPICE, or similar simulation and modeling tools.
  • Hands-on experience in prototype bring-up, debugging, and functional verification.
  • 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).
  • Strong understanding of the underlying physics in current silicon electronics.
  • Knowledge of machine learning, computer vision, and computer architecture.
  • Experience implementing machine learning models in current frameworks (e.g. Tensorflow).