Machine Learning Scientist
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)