Cheminformatics & Synthesis Prediction

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About the role

Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world in order to answer the globe’s growing challenges in health, energy, food, materials, and more. Our bioengineers make use of an in-house automated foundry for designing and building new organisms. Senior Engineer I, Cheminformatics & Synthesis Prediction Boston, Massachusetts About Ginkgo Datapoints Ginkgo Datapoints, a business unit within Ginkgo Bioworks, is ushering in the coming era of AI-backed biotechnology breakthroughs. By leveraging Ginkgo’s automation and digital infrastructure, Datapoints builds high-quality, large-scale datasets and technical capabilities that accelerate drug discovery and development. The Small Molecules team is looking for a cheminformatics scientist to support the computational work of building and navigating makeable chemical spaces. This includes building the space itself, developing tools that help scientists search and use it, and predicting the outcomes of chemical reactions that connect design to synthesis. Role Overview We are hiring a Senior Engineer I, Cheminformatics to develop and operate the cheminformatics workflows behind Ginkgo’s makeable chemical space and reaction-prediction capabilities. This is a hands-on production role spanning two connected areas: building and maintaining large makeable chemical spaces through reaction enumeration, and integrating, evaluating, and improving reaction-prediction workflows such as retrosynthesis and reaction-condition prediction. You will work with reaction templates, molecular representations, functional-group logic, vendor building blocks, predictive models, and reaction data that feeds the design–make–test loop. The ideal candidate combines practical cheminformatics engineering, experience with reaction prediction, and strong chemistry fluency. Internal chemists provide deep synthetic and medicinal chemistry expertise; your role is to translate their questions into reliable computational workflows and explain what the systems did and why. This is not an ML research position. We prefer to adopt or adapt published methods and open-source tools before building new systems. Key Responsibilities Makeable chemical space • Develop and improve reaction-enumeration workflows, including reaction SMARTS templates, functional-group gating, building-block curation, and production runs. • Work with large vendor catalogs while balancing chemical coverage, price, availability, lead time, and data quality. • Improve treatment of regioisomers, stereochemistry, resolution limits, and other sources of ambiguity in enumerated chemical space. • Build reliable workflows for structure handling, reaction execution, sanitization, identifiers, SDF files, and metadata. Reaction prediction and design–make–test workflows • Integrate and evaluate approaches for retrosynthesis, synthetic success, reaction-condition prediction, and related reaction modeling tasks. • Assess models and workflows for calibration, coverage, applicability domain, and practical usefulness; surface uncertainty and risk flags rather than bare point estimates. • Help connect predicted reactions and enumerated compounds to experimental design, make–test workflows, and downstream learning. • Consolidate reaction data—including conditions, yields, failed reactions, and provenance—into a shared, machine-readable source that supports future model improvement. Production platform and collaboration • Write maintainable code and contribute to service-oriented systems, deployment workflows, and data pipelines. • Partner with internal chemists and cross-functional teams to translate scientific questions into reliable computational workflows and interpret results. • Scope an

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