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Salary: USD 182,000 - 210,000 / annual
Citrine Informatics is an AI-driven materials science company serving 10 of the top 20 chemical companies globally. The Data & AI Research Engineering (DARE) team is uniquely interdisciplinary, working at the intersection of materials science, machine learning, and software engineering. This team is responsible for researching, developing, testing, implementing, and maintaining the core materials-aware machine learning functionality that powers the Citrine Platform.
As a Data & AI Research Engineer L4, you will be a senior individual contributor and mentor within the DARE team. You will write and maintain Python ML code, research and prototype new materials-aware ML models and features, and design high-performance, scalable ML systems. You will collaborate closely with other engineers through code reviews and best practices, mentor other developers, and work in multi-functional teams from concept to delivery. Example projects include improving core Python libraries for industrial-scale materials science ML applications, collaborating with the Product team to shape future AI capabilities, improving model interpretability and uncertainty quantification, enhancing inverse design capabilities for materials synthesis, and publishing research papers in collaboration with the External Research and Development team.
You will be responsible for testing and analyzing model impact and performance, working across the company to translate ideas to math to code and back again. The role offers the opportunity to contribute to sustainable materials and chemicals that are critical to both planetary and industry futures.
REQUIREMENTS:
- 8+ years of professional experience OR 3+ years with MS/PhD in a quantitative discipline
- Proficient in a programming language such as Python or Scala
- Proven history of implementing AI solutions for customers with quantifiable results
- Experience solving scientific problems with computational techniques
- Ability to communicate complex technical concepts and design choices to any audience
- In-depth knowledge of how core ML algorithms work and their design (random forest, neural networks, etc.)
- Ability to write tested, production-quality code
- Legally eligible to work in the United States
PREFERRED SKILLS:
- Background in materials science, chemistry, or physics
- Experience with uncertainty quantification in ML
- Familiarity with Bayesian methods
- Experience with optimization algorithms
- Publication record in ML or scientific computing
