Cheminformatics & Synthesis Prediction

The role

You will develop and operate cheminformatics workflows for makeable chemical space and reaction-prediction capabilities. This includes building reaction-enumeration workflows and integrating retrosynthesis and reaction-condition prediction models.

What you will do

• Develop and improve reaction-enumeration workflows including reaction SMARTS templates and functional-group gating.
• Work with large vendor catalogs balancing coverage, price, availability, and data quality.
• Improve treatment of regioisomers, stereochemistry, and other sources of ambiguity.
• Build workflows for structure handling, reaction execution, and metadata.
• Integrate and evaluate approaches for retrosynthesis and reaction-condition prediction.
• Assess models for calibration, coverage, and applicability domain.
• Connect predicted reactions and enumerated compounds to experimental design and make-test workflows.
• Consolidate reaction data into a shared, machine-readable source.
• Write maintainable code and contribute to service-oriented systems and data pipelines.
• Partner with internal chemists to translate scientific questions into computational workflows.

Required qualifications

• Ph.D. in cheminformatics, computational chemistry, organic chemistry, medicinal chemistry, or a closely related quantitative field, plus 3 years of relevant industry or postdoctoral experience; or an
• Strong practical experience in cheminformatics and reaction prediction, including retrosynthesis, reaction-condition prediction, or synthetic accessibility.
• Experience working with reaction SMARTS, functional-group classification, molecular representations, and chemical structure data.
• Production experience with RDKit or an equivalent cheminformatics toolkit.
• Experience working with large chemical or vendor building-block catalogs.
• Strong Python skills and experience writing code that others can run and maintain.
• Familiarity with service-oriented software, Docker, environment-based configuration, and data pipelines.
• Working knowledge of common medicinal-chemistry synthetic transformations.
• Clear technical communication skills and a disciplined approach to documentation, provenance, and reproducibility.

Nice to have

• Experience with large makeable or synthesizable chemical spaces, whether commercial or in-house.
• Experience operating a combinatorial library-enumeration pipeline or reaction-prediction workflow in production.
• Experience evaluating predictive models, including uncertainty estimation, calibration, applicability domain, or ranked candidate generation.
• Experience with regiochemistry, stereochemistry, chemical registration systems, ELN/LIMS integration, or chemical data standards.
• Experience connecting computational design to wet-lab results in a design–make–test–learn environment.
• Contributions to open-source cheminformatics or experience evaluating third-party chemistry platforms.
• Experience building and optimizing Model Context Protocols (MCPs), agentic frameworks, or integrating cheminformatics tools with LLM-based workflows.

Benefits

• Competitive compensation
• Generous equity in a public company
• Medical, dental & vision coverage
• Health spending accounts
• Voluntary benefits
• Leave of absence policies
• 401(k) program with employer contribution
• 8 paid holidays
• Full-week winter shutdown
• Unlimited Paid Time Off policy

About the company

Ginkgo is constructing, editing, and redesigning the living world to answer challenges in health, energy, food, materials, and more. Ginkgo Datapoints builds high-quality, large-scale datasets and technical capabilities that accelerate drug discovery and development.