AI/ML Engineer, Drug Discovery

We’re looking for a hands-on AI/ML Engineer to support drug discovery research through applied data science, machine learning, and informatics. This is a strong fit for someone who enjoys working directly with scientists, translating open-ended research questions into practical models and tools, and building workflows that hold up under real use.

The role spans the full solution lifecycle - from data preparation and exploratory analysis through feature engineering, model development, validation, and deployment - with room to grow into production AI systems, LLM-based applications, and agentic workflows as the work evolves.

What You’ll Do

• Work directly with research scientists to scope high-value problems and turn scientific questions into analytical or ML approaches.
• Build, test, and refine predictive models using both structured and unstructured scientific data.
• Design reproducible pipelines for ingesting, cleaning, integrating, and preparing data, including feature generation and quality checks.
• Apply data science and cheminformatics techniques to support discovery research and decision-making.
• Run exploratory analyses, communicate findings clearly, and recommend suitable modeling strategies.
• Write clean, maintainable Python and SQL, contributing to shared analytical tools, services, and APIs.
• Partner with engineers to bring models and workflows into stable production or research environments.
• Apply solid practices around experiment tracking, model versioning, testing, documentation, monitoring, and retraining.
• Contribute to AI-enabled applications - including LLM, copilot, or agent-based tools - where they fit the use case.
• Document data sources, assumptions, methods, and results for both technical and scientific audiences.
• Balance fast iteration with data quality, reproducibility, and operational reliability.

• Bachelor’s degree in computer science, data science, mathematics, statistics, computational science, cheminformatics, bioinformatics, or a related quantitative field.
• 4+ years of relevant experience applying data science, machine learning, informatics, or software engineering to real-world problems.
• Solid hands-on skills in Python and SQL, along with common data analysis and ML libraries.
• Track record preparing complex datasets, engineering features, building models, and evaluating performance.
• Ability to turn ambiguous scientific or business questions into clear, actionable technical plans.
• Experience with reproducible workflows and standard engineering practices - version control, testing, code review.
• Strong written and verbal communication, including explaining technical work to non-technical or scientific audiences.
• A collaborative style, genuine curiosity, and comfort working in a fast-moving research setting.