Applied Machine Learning Engineer – Chemistry & Data Science

We’re seeking an Applied Machine Learning Engineer with a strong chemistry, chemical engineering, or materials science background to lead experimental optimization campaigns. This hybrid role blends chemistry and data science—turn messy experimental data into actionable insights using Python, Bayesian optimization, and Design of Experiments (DOE) principles.

What You’ll Do:

Lead optimization campaigns from dataset prep to insight delivery.
Run Bayesian Optimization and Active Learning loops for efficient experimentation.
Combine DOE methods with ML approaches for high-quality results.
Interpret and communicate model outputs to guide decision-making.
Collaborate across teams and ensure data integrity.

What You Bring:

Master’s or PhD in Chemistry, Chemical Engineering, Materials Science, or related.
Strong Python/data science skills (pandas, numpy, scikit-learn, PyTorch).
DOE experience and comfort with experimental/chemical data.
Familiarity with Bayesian Optimization and interpreting ML results.
Excellent communication skills for technical and non-technical audiences.

Bonus Skills:

Cheminformatics tools (RDKit), causal inference, visualization, R&D collaboration.

Why This Role Matters:

Accelerate experimentation by bridging chemistry and ML.
Translate complex results into actionable scientific insights.
Contribute to reproducible, high-impact experimental outcomes.

Benefits:

Competitive salary + equity
Health benefits
Fully remote, collaborative small team
Growth and promotion opportunities