Design and build the core scientific libraries that enable our users to apply machine learning to solve the world’s most important materials science problems at an industrial scale.

About Citrine

At Citrine, we’re using generative Artificial Intelligence to enable our customers to usher in the next generation of sustainable, high-performing materials and chemicals.

We’re the leader in AI for materials and chemicals. Our no-code SaaS platform gives product developers, researchers, and engineers access to domain-specific data management and AI tools. Our models propose new ways to combine ingredients and processes that are most likely to outperform, helping companies discover and deploy innovative, sustainable, high-performing materials and chemicals up to 98% faster than traditional R&D approaches.

Citrine regularly wins awards for innovation and sustainability, earning spots on the CB Insights AI 100 list and the Inc. 5000 list of fastest-growing private companies in the US. Our customers include leading organizations such as LyondellBasell, Panasonic, Michelin, Synthomer, and Solvay. We also collaborate with researchers from world-renowned institutions on cutting-edge research at the intersection of AI and the physical sciences. Our team is ambitious with our goals, passionate about our vision, driven by our sustainability mission, and eager to grow and learn from each other. We’re headquartered in California, with team members throughout North America and Europe. We’re growing quickly, and we’re looking for the best to join us!

About the Role

Data & AI Research Engineering is a uniquely interdisciplinary team working at the intersection of materials science, applied mathematics, and software engineering. We are responsible for researching, developing, testing, implementing, and maintaining scientific methods that form the core materials-aware machine learning functionality that powers the Citrine Platform. We collaborate extensively across the company — other teams rely on us throughout the product lifecycle to translate ideas to math to code and back again.

Responsibilities include:

- Writing and maintaining Scala ML code, with some additional Python

- Collaborate closely with other engineers, frequently reviewing code and best practices

- Mentor other developers

- Design high-performance, scalable systems

- Test & analyze the impact and performance of your software

- Work in a multi-functional team on features from concept to delivery

Example projects:

- Improve and maintain our core Scala libraries that enable our users to apply machine learning to solve the world’s most important materials science problems at an industrial scale

- Collaborate with our External Research and Development (ERD) team to write and publish papers related to the work we’re doing

- Improve the interpretability of machine learning models and develop tools to communicate that information to users

- Develop ways to efficiently explore the complex parameter spaces that characterize real-world materials synthesis problems

- Collaborate with our Product team to shape the future of our AI capabilities and support our customers’ unique use cases

- Develop new methods to quantify uncertainty in machine learning predictions