Computational Synthetic Chemist

Boston, Massachusetts

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 synthetic organic chemist to support computationally enabled synthetic chemistry. This role sits between the high-throughput and direct-to-biology core and the cheminformatics function, connecting route design, practical synthesis, reaction prediction, automated experimentation, and structured data capture.

We are hiring a Senior Engineer I, Synthetic Chemistry to design and execute multi-step syntheses of novel drug-like molecules, including singleton analogs and small focused sets that cannot be made in plate format.

You will own route selection, execution, purification, and characterization end to end. Computational route planning, retrosynthesis, condition prediction, and automated or high-throughput experimentation resources will be standard working tools-not occasional aids. You will consume model output, judge when it is useful, correct it when it is not, and feed outcomes back into the planning and prediction workflows.

The ideal candidate is an accomplished bench chemist who is also fluent in computer-aided synthesis planning (CASP), reaction-prediction tools, and reaction data. You will be the resident synthetic-chemistry judgment for this part of the design-make-test loop and will mentor junior chemists using the same tools. This is not a software or ML research position; it is a chemistry role for someone who wants to make computational workflows genuinely useful in practice.

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  Design, select, and execute practical multi-step routes to novel drug-like singleton compounds and focused sets under real constraints on time, materials, building blocks, and available resources.

  
  
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  Apply a broad synthetic-organic chemistry toolkit and own purification and characterization through delivery of high-quality, well-characterized compounds on schedule.

  
  

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  Use computer-aided synthesis planning, retrosynthesis, and reaction-condition tools routinely; critically evaluate proposed routes and predictions and explain when they are implausible, impractical, or outside reliable coverage.

  
  
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  Use chemical structure and reaction representations and building-block catalogs to constrain plans to realistic starting materials, and design or interpret HTE and other reaction-optimization experiments.

  
  
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  Feed practical outcomes and corrected synthetic judgment back into planning, prediction, and cheminformatics workflows.

  
  

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  Record every campaign, including failures, in structured, machine-readable form covering conditions, stoichiometry, outcomes, analytical data, and negative results.

  
  
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  Design chemistry for automation and parallel execution where useful, while recognizing when a route genuinely requires manual bench work.

  
  
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  Collaborate with chemists, computational scientists, and software engineers to define useful tools, make go/no-go heuristics explicit, and mentor junior chemists.

  
  

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  Ph.D. in synthetic organic chemistry, medicinal chemistry, or a closely related field, plus 3 years of relevant industry or postdoctoral experience; or an M.S. with 6 years, or a B.S. with 9 years, of relevant experience.

  
  
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  Demonstrated ability to design and execute multi-step syntheses, including routes of approximately five or more steps through characterized final compounds, and to deliver compounds on schedule.

  
  
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  Independent route-design judgment under real constraints on materials, time, building blocks, and execution resources, supported by a broad synthetic-organic reaction toolkit and strong purification and characterization skills.

  
  
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  Routine hands-on use of computer-aided synthesis planning or retrosynthesis tools, with the judgment to recognize when a proposed route or prediction should not be trusted.

  
  
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  Fluency with chemical structure and reaction data, including molecular and reaction representations, compound registration, stereochemistry, salts, tautomers, and building-block catalogs.

  
  
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  Experience with reaction screening, HTE, design of experiments, or related optimization workflows, plus disciplined structured capture of conditions, outcomes, analytical results, and failures.

  
  

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  You understand that computational models capture the current state of knowledge: they will never be perfect, but they can support learning and better decisions. You know the difference between useful guidance and an output that is not reliable enough to use.

  
  
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  You balance skepticism with practical progress. You make thoughtful go/no-go calls, state the limits of your judgment, and route unresolved questions to the right person or experiment rather than resolving them by assertion.

  
  
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  You are intellectually curious and impatient with the status quo, with a passion for establishing and advancing state-of-the-art chemistry workflows in practical production settings.

  
  

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  Python scripting with tools such as RDKit, pandas, or Jupyter for manipulating compound sets, parsing analytical output, or prototyping analyses.

  
  
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  Experience running chemistry on robotic platforms, liquid handlers, flow systems, or other automated resources.

  
  
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  Industrial HTE or nanoscale reaction-screening experience.

  
  
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  Prior work in a CRO, fee-for-service, or similarly schedule-driven environment.

  
  
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  Experience with ELN/LIMS configuration or reaction-data model definition.

  
  
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  Experience scaling a validated route from milligram to multi-gram quantities.

  
  
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  Experience with inline or process monitoring such as ReactIR, benchtop NMR, or online LC-MS.

  
  
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  Experience mentoring or supervising junior chemists.

  
  
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  Publications or patents describing multi-step routes to biologically active compounds.

  
  

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  Help build the world's most computationally enabled synthesis capability, combining expert chemistry with route planning, reaction prediction, automation, and learning.

  
  
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  Help connect route design, reaction prediction, synthesis, testing, and learning across Ginkgo's automated platforms.

  
  
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  Develop workflows to capture, structure, and learn from the reaction data generated by autonomous laboratory systems.

  
  
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  Join a technically ambitious, cross-functional team building the computational foundations for AI-enabled drug discovery.

  
  
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  Receive competitive compensation, generous equity in a public company, and a robust benefits package.