Machine Learning Scientist

  • Palo Alto, United States

Machine Learning Scientist

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 talented Machine Learning Scientist with strong first-principles understanding of neural networks to collaborate with our optics and electronics teams in designing Fathom computers. Examples of responsibilities are listed below.

  • Implement cutting-edge machine learning algorithms on our unique hardware
  • Think through several layers of abstraction all the way from lower level circuits through instruction set architecture
  • Design new ML algorithms for future hardware systems
  • Develop, adapt and map general machine learning algorithms based on features of our hardware
  • Invent new models that combine unsupervised and supervised learning with the kind of creativity usually reserved for blue-sky research projects

Note: This is not an entry-level position.


  • BS/MS/PhD, or equivalent knowledge and 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 including RNN, CNN, RL.
  • Strong analytical skills (probability, optimization, etc.)
  • Experience working with large models.
  • Depth and breadth of knowledge in the field, including recent research results.
  • Knowledge of current frameworks (e.g. TensorFlow, Theano, etc.)
  • Efficient and effective written and verbal communication; ability to collaborate on a multidisciplinary system with scientists of different backgrounds.


  • Some familiarity with computer architecture and implementing algorithms on multi-core CPUs, GPUs, MPI clusters, and/or heterogenous clusters
  • Significant academic or work experience (including but not limited to published papers, patents, industry, and personal projects)