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 talented Machine Learning Framework Engineer with strong first-principles understanding of neural networks and deep learning to collaborate with our optics and electronics teams in simulation, design and implementation of novel optoelectronic hardware.
Areas of contribution
- Implementing novel machine learning algorithms on our unique hardware
- Analyzing the performance of state of the art deep learning models on fathom hardware
- Writing new ops and kernels to emulate the unique properties of our hardware on current deep learning frameworks
- Developing, adapting, and mapping general machine learning algorithms based on features of our hardware
- BS/MS/PhD, or equivalent experience in CS, EE, or related fields (e.g. statistics, applied math, computational neuroscience)
- Deep passion and fundamental understanding of design, algorithms, and data structures in modern machine learning and AI
- Strong understanding of the fundamentals of neural networks and common general algorithms
- Strong analytical skills (probability, optimization, etc.)
- Experience working with large deep learning models
- Extensive knowledge of current deep learning frameworks (e.g. TensorFlow, PyTorch, etc.) - from training SOTA models to implementing custom graph operators or making low level modifications to the framework.
- Excellent communication skills and ability to collaborate on a complex cross-disciplinary system
- Drive to build something that hasn't been built before
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 :)