Redwood City, California
over 1 year ago
Scientific Machine Learning Engineer (Bayesian Inferencing, Materials Science)
Our award-winning company is setting out to disrupt the development of cutting-edge materials. With so much innovation happening worldwide in different sectors, the demand for solving the planet's most difficult materials informatics' problems has never been greater. As a result of this, we are searching for a Scientific Machine Learning Engineer to work at the intersection of applied mathematics, software engineering and materials science to help build a first of its kind platform which fast-tracks development of next generation materials by systematically leveraging huge sets of data.
Based in the Bay Area, we are hugely proud of what we have achieved in the last 24 months and are looking to set our sights even higher for the years to come. As a Scientific Machine Learning Engineer, you will join us in our vision of cultivating a data ecosystem that fast-tracks new developments in materials manufacturing, enabling a more efficient, sustainable world. Within our team, you will be able to work heavily within Machine Learning optimization and productionizing Machine Learning models. Ideally, we are looking for a Scientific Machine Learning Engineer who comes from a numerical programming background [Python] and has some experience with one of the following: Bayesian Inference, Gaussian Process, Stochastics, Numerical Programming, Optimization, Distributed Systems.
We can offer a Scientific Machine Learning Engineer:
- A chance to be a part of developing a platform which will change the way develop materials, essentially shaping the future world.
- A culture of diversity, creativity and inclusivity - all working together under one shared vision.
- Intellectual challenges that you would not experience in most companies.
- No corporate bureaucracy!
- Working in our awesome office, though we will consider remote workers.
Key Skills: Scientific Machine Learning Engineer, Software Engineer, Software Developer, Design, Architecture, Mathematics, Statistics, Informatics, Computer Science, Numerical Programming, Numerical methods, science, materials science, Production-quality software, Machine Learning, Optimization, Models, Distributed Systems, Parallel Computing, Large scale systems, Python, Scala, Java.