Machine Learning Scientist

Fathom Computing

Are you a machine learning researcher excited to architect history-accelerating hardware? We want to speed up training and inference by many orders of magnitude and alter the course of human history. Help us develop unique tools with massive advantages over existing electronic computers. Work alongside systems engineers, computer architects, optical physicists, and materials engineers.


Help design and implement hardware to run cutting edge problems in machine learning

Develop, adapt and map general machine learning algorithms in light of our specific hardware features

Invent new models that combine unsupervised and supervised learning with the kind of creativity usually reserved during novel computing paradigm creation

Developing new machine learning algorithms to exploit novel optical processor architectures

Deep passion and fundamental understanding of design, algorithms, and data structures in modern and sub-modern machine learning and AI fields

Strong understanding the fundamentals of neural networks and all common general algorithms including: RNN, CNN, RCNN, RL

Strong analytical skills (probability, algebra, optimization, etc.)

Experience working with large models

Depth and breadth of knowledge in the field

Familiarity with details of computer architecture and implementing algorithms on multi-core CPUs, clusters (MPI), GPUs, heterogenous clusters

High creativity and productivity

Efficient and effective written and verbal communication

You love doing great work, especially with the chance to have a major societal impact.

You’ve worked on cool projects and you’d love to tell us about them!

Extensive experience and accomplishment are recommended but are not an absolute requirement. Master’s or PhD, or equivalent knowledge in computer science, electrical engineering or related fields (statistics, applied math, computational neuroscience)

Bonus: won Kaggle competitions, awards, etc

Some knowledge of current frameworks such as TensorFlow, Caffe, etc

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