Role OverviewLead the design and execution of optimization and active-learning campaigns across chemistry, formulation, and process development. Collaborate with R&D and manufacturing on framing optimization problems with design spaces, decision variables, objectives, and hard and soft constraints.
What You Will Do
Design and coordinate sequential experiment campaigns using Bayesian optimization and active learning, accounting for operational variabilities and constraints. Drive closed-loop optimization that connects surrogate models to experimentation, with emphasis on decision quality, exploration versus exploitation, and actionable recommendations.
Why It Might Be a Fit
7+ years experience in ML modeling or optimization experience for chemistry, formulation, process, or manufacturing problems. Demonstrated expertise in Bayesian optimization and Gaussian processes, including kernels, acquisition functions, and batch, multi-objective, and constrained settings.
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field
- 7+ years experience in ML modeling or optimization experience for chemistry, formulation, process, or manufacturing problems
- Demonstrated expertise in Bayesian optimization and Gaussian processes, including kernels, acquisition functions, and batch, multi-objective, and constrained settings
- Strong Python skills and experience with mainstream Python-based ML and Bayesian optimization frameworks and tools
- Active use of AI-assisted coding and other AI tools in daily work, with familiarity with emerging ML methods including LLM and agentic approaches
Benefits
- Equal Opportunity, Affirmative Action employer
- Drug test and background investigation required for employment
- Periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies
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