Uncountable helps the world’s largest manufacturers drive innovation. By leveraging advanced artificial intelligence techniques, Uncountable augments the traditional R&D approach to get better material and chemical products to the market in half the time. Founded by MIT and Stanford machine learning experts, Uncountable is building a great company attacking an untapped R&D market. We work with companies of all sizes, from innovative startups to Fortune 500 manufacturers, delivering a proven solution that creates tremendous value for research organizations.

We are looking to build out our world-class technical team. 

Machine Learning Engineer

Location: San Francisco, CA     Commitment: Full-time     Salary Range: $150,000 - $220,000 / year

Description: Uncountable is seeking machine learning engineers who are passionate about statistics and optimization. Our goal is to revolutionize industrial research and development with artificial intelligence.  We're looking for motivated engineers who can help us automate the experimental process of Fortune 500 companies.

Primary Responsibility: Your primary responsibility will be to improve our Bayesian optimization algorithms and prototype better techniques for designing experiments with sparse data.

- MS or PhD in CS, Statistics, EE, Mathematics, or Physics
- Expertise in statistical machine learning theory
- Experience with software development
- Interest in applied mathematics, stochastic modeling, or optimization

Preferred Qualifications:
- Familiarity with global optimization problems
- Understanding of Bayesian optimization
- Experience with numerical computing such as with Numpy, Julia, etc.
- Experience with automatic differentiation libraries such as PyTorch, Theano, Caffe, TensorFlow, etc.


Important Clarification:
Uncountable is not a deep learning company nor a big data company.  Most of the problems we work with have fewer than 100 data points and almost all have fewer than 1000.
We take pride in our ability to be as accurate as possible with very little data.  If you're excited by very challenging statistics or optimization problems, this is the place for you.


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