Machine Learning Hardware Architect
Palo Alto, California /
Fathom Computing is developing high-performance machine learning computers built to run both training and inference workflows for large-scale neural networks. As ML 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 machine learning hardware architect with strong first principles approach to computer architecture and microarchitectural design. You will collaborate closely with colleagues from other disciplines (e.g. machine learning, optics) to contribute to design and implementation of novel optoelectronic hardware.
Areas of contribution
- Digital silicon architecture, IC design/implementation/validation, and working with SW to build accurate simulations
- Drive the design and implementation of the electronic aspects of a novel, complex, high-performance, deeply-integrated computing system
- Contribute to cross disciplinary optimization considering aspects from ML algorithms to physical implementation
- MS/PhD degree in EE or equivalent
- Familiarity with deep learning algorithms and ML accelerators
- Experience in architecture, design, tape-out, and testing of VLSI ICs
- Proficiency in HDL languages, such as Verilog, VHDL, or Chisel
- Excellent communication skills and ability to collaborate on complex cross-disciplinary systems
- Excellent analytical, problem-solving, and organizational skills
- Drive to build something that hasn't been built before
- Significant academic or work experience in computer architecture and implementation of machine learning hardware accelerators
- Experience in atypical architectures is welcome (e.g. neuromorphic)
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 :)