Ginkgo Bioworks

<div><p>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. </p>
<hr />
<p> </p></div><h2><strong>Scientist, AI/ML — Antibody Developability</strong></h2>
<p>Datapoints Team | Ginkgo Bioworks | Boston, MA</p>
<h2><strong>About the Role</strong></h2>
<p>Datapoints, Ginkgo Bioworks' Bio × AI data generation platform, is seeking a Scientist to build and benchmark machine learning models that predict and design antibody developability. You will sit between our PROPHET-Ab high-throughput biophysical platform and the models it enables, working with both newly generated customer datasets and the GDPa public dataset series (clinical IgGs, sequence-diverse natural IgGs, bispecifics, VHH-Fcs), cross-format prediction, and generative design campaigns.</p>
<p>This computational role requires deep understanding of biophysical assay measurements, noise characteristics, and rigorous evaluation strategies for small-scale datasets. Ideal candidates are recent Ph.D. graduates with strong applied ML proficiency and interest in protein biophysics, motivated by integrated experimental and computational design.</p>
<h2><strong>Key Responsibilities</strong></h2>
<ul>
<li>Develop and validate predictive models for developability using PLM embeddings, structural features, and physicochemical descriptors.</li>
<li>Implement leakage-aware evaluation frameworks for rigorous performance assessment of small-scale datasets.</li>
<li>Evaluate cross-format transferability (IgG to VHH-Fc, bispecifics) and deploy data-efficient training strategies.</li>
<li>Perform rigorous QC on biophysical assay data to identify outliers and maintain high data integrity for modeling.</li>
<li>Maintain and develop automated pipelines to ensure reproducibility and facilitate technical reporting.</li>
<li>Disseminate findings through technical reviews, peer-reviewed manuscripts, and external scientific presentations.</li>
</ul>
<h2><strong>Qualifications</strong></h2>
<h3><strong>Required</strong></h3>
<ul>
<li>Ph.D. in Computational Biology, Bioinformatics, ML, Biophysics, or related quantitative field with emphasis on applied ML.</li>
<li>Proficiency in Python and the scientific computing stack (NumPy, pandas, PyTorch/TensorFlow).</li>
<li>Demonstrated experience in supervised learning on biological datasets, including expertise in cross-validation and bias mitigation.</li>
<li>Knowledge of protein representations, including PLM embeddings (e.g., ESM, AbLang) and structural featurization.</li>
<li>Ability to analyze biophysical data with respect to signal-to-noise ratios and experimental dynamic range.</li>
<li>Effective communication across multidisciplinary teams and ability to manage concurrent technical objectives.</li>
</ul>
<h3><strong>Preferred</strong></h3>
<ul>
<li>Familiarity with antibody formats (VHH, scFv), therapeutic developability liabilities, and relevant characterization assays.</li>
<li>Experience with state of the art supervised techniques such as tabular foundation models.</li>
<li>Exposure to generative sequence modeling, diffusion models, and reward-based steering.</li>
<li>Experience with protein structure prediction (AlphaFold, ABodyBuilder) and surface patch analysis.</li>
<li>Prior engagement with high-throughput experimental design and collaborative data generation.</li>
<li>Record of scientific publication and software engineering fundamentals (Git, testing, cloud compute).</li>
</ul>
<p>Location: Boston, MA (Hybrid).</p>
<hr />
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<p><em>The base salary range for this role is $130,600 – $183,800. Actual pay within this range will depend on a candidate's skills, expertise, and experience. We also offer company stock awards, a comprehensive benefits package including medical, dental &amp; vision coverage, health spending accounts, voluntary benefits, leave of absence policies, 401(k) program with employer contribution, 8 paid holidays in addition to a full-week winter shutdown and unlimited Paid Time Off policy.</em></p>
</div>
<div>
<p><strong>Ginkgo has implemented a return to office policy effective October 1, 2025 with required in-office days 5x per week. This policy applies to all employees who live within 50 miles of Ginkgo’s offices in Boston, MA, Emeryville, CA and West Sacramento, CA.</strong></p>
</div>
</div>
</div>
</div>
</div>
</div>
</div><div><div> </div>
<div>It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees, employment applicants, and EOE disability/vet. </div>
<div> </div>
<div><hr /></div>
<div> </div>
<div><span><strong>Privacy Notice</strong></span></div>
<div>I understand that I am applying for employment with Ginkgo Bioworks and am being asked to provide information in connection with my application. I further understand that Ginkgo gathers this information through a third-party service provider and that Ginkgo may also use other service providers to assist in the application process.  Ginkgo may share my information with such third-party service providers in connection with my application and for the start of employment.  Ginkgo will treat my information in accordance with Ginkgo's <a href="https://www.ginkgobioworks.com/concentric-privacy-policy/" target="_blank" rel="noopener noreferrer ugc">Privacy Policy</a>.  By submitting this job application, I am acknowledging that I have reviewed and agree to Ginkgo's <a href="https://www.ginkgobioworks.com/concentric-privacy-policy/" target="_blank" rel="noopener noreferrer ugc">Privacy Policy</a> as well as the privacy policies of the third-party service providers used by Ginkgo's associated with the application process.</div></div>

Scientist, AI/ML — Antibody Developability

<div><p>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. </p>
<hr />
<p> </p></div><p></p>
<h2><strong>Senior Engineer I, Cheminformatics &amp; Synthesis Prediction</strong></h2>
<p><strong>Boston, Massachusetts</strong></p>
<h2><strong>About Ginkgo Datapoints</strong></h2>
<p><strong>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.</strong></p>
<p><strong>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.</strong></p>
<h2><strong>Role Overview</strong></h2>
<p><strong>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.</strong></p>
<p><strong>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.</strong></p>
<p><strong>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.</strong></p>
<h2><strong>Key Responsibilities</strong></h2>
<h3><strong>Makeable chemical space</strong></h3>
<ul>
<li>
<p><strong>Develop and improve reaction-enumeration workflows, including reaction SMARTS templates, functional-group gating, building-block curation, and production runs.</strong></p>
</li>
<li>
<p><strong>Work with large vendor catalogs while balancing chemical coverage, price, availability, lead time, and data quality.</strong></p>
</li>
<li>
<p><strong>Improve treatment of regioisomers, stereochemistry, resolution limits, and other sources of ambiguity in enumerated chemical space.</strong></p>
</li>
<li>
<p><strong>Build reliable workflows for structure handling, reaction execution, sanitization, identifiers, SDF files, and metadata.</strong></p>
</li>
</ul>
<h3><strong>Reaction prediction and design–make–test workflows</strong></h3>
<ul>
<li>
<p><strong>Integrate and evaluate approaches for retrosynthesis, synthetic success, reaction-condition prediction, and related reaction modeling tasks.</strong></p>
</li>
<li>
<p><strong>Assess models and workflows for calibration, coverage, applicability domain, and practical usefulness; surface uncertainty and risk flags rather than bare point estimates.</strong></p>
</li>
<li>
<p><strong>Help connect predicted reactions and enumerated compounds to experimental design, make–test workflows, and downstream learning.</strong></p>
</li>
<li>
<p><strong>Consolidate reaction data—including conditions, yields, failed reactions, and provenance—into a shared, machine-readable source that supports future model improvement.</strong></p>
</li>
</ul>
<h3><strong>Production platform and collaboration</strong></h3>
<ul>
<li>
<p><strong>Write maintainable code and contribute to service-oriented systems, deployment workflows, and data pipelines.</strong></p>
</li>
<li>
<p><strong>Partner with internal chemists and cross-functional teams to translate scientific questions into reliable computational workflows and interpret results.</strong></p>
</li>
<li>
<p><strong>Scope and review external or consultant work with clear specifications and acceptance criteria.</strong></p>
</li>
<li>
<p><strong>Work on commercial digital products by integrating pricing and ordering data and functionalities, and deploying and maintaining related services and tools.</strong></p>
</li>
<li>
<p><strong>Design, build, and optimize Model Context Protocols (MCPs) and agentic frameworks to support LLM-based work and integrate cheminformatics tools into automated workflows.</strong></p>
</li>
</ul>
<h2><strong>Minimum Qualifications</strong></h2>
<ul>
<li>
<p><strong>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 M.S. with 6 years, or a B.S. with 9 years, of relevant experience.</strong></p>
</li>
<li>
<p><strong>Strong practical experience in cheminformatics and reaction prediction, including experience with retrosynthesis, reaction-condition prediction, synthetic accessibility, or related workflows.</strong></p>
</li>
<li>
<p><strong>Experience working with reaction SMARTS, functional-group classification, molecular representations, and chemical structure data.</strong></p>
</li>
<li>
<p><strong>Production experience with RDKit or an equivalent cheminformatics toolkit.</strong></p>
</li>
<li>
<p><strong>Experience working with large chemical or vendor building-block catalogs and understanding the trade-offs involved in coverage, scale, and data quality.</strong></p>
</li>
<li>
<p><strong>Strong Python skills and experience writing code that others can run and maintain.</strong></p>
</li>
<li>
<p><strong>Familiarity with service-oriented software, Docker, environment-based configuration, and data pipelines.</strong></p>
</li>
<li>
<p><strong>Working knowledge of common medicinal-chemistry synthetic transformations and the ability to collaborate effectively with synthetic chemists.</strong></p>
</li>
<li>
<p><strong>Clear technical communication skills and a disciplined approach to documentation, provenance, and reproducibility.</strong></p>
</li>
</ul>
<h2><strong>Working Style</strong></h2>
<ul>
<li>
<p><strong>You understand that models are mathematical representations of the current state of knowledge: they will never be perfect, but they can support learning and better decisions. You know how to distinguish useful signal from an output that is not reliable enough to use, and communicate uncertainty and limitations clearly.</strong></p>
</li>
<li>
<p><strong>You balance scientific rigor with practical progress. You make thoughtful trade-offs, avoid overclaiming, and collaborate clearly with chemists and engineers to turn imperfect computational tools into useful workflows.</strong></p>
</li>
<li>
<p><strong>You are intellectually curious and impatient with the status quo, with a passion for establishing and advancing state-of-the-art computational methods in practical production settings.</strong></p>
</li>
</ul>
<h2><strong>Preferred Qualifications</strong></h2>
<ul>
<li>
<p><strong>Experience with large makeable or synthesizable chemical spaces, whether commercial or in-house.</strong></p>
</li>
<li>
<p><strong>Experience operating a combinatorial library-enumeration pipeline or reaction-prediction workflow in production.</strong></p>
</li>
<li>
<p><strong>Experience evaluating predictive models, including uncertainty estimation, calibration, applicability domain, or ranked candidate generation.</strong></p>
</li>
<li>
<p><strong>Experience with regiochemistry, stereochemistry, chemical registration systems, ELN/LIMS integration, or chemical data standards.</strong></p>
</li>
<li>
<p><strong>Experience connecting computational design to wet-lab results in a design–make–test–learn environment.</strong></p>
</li>
<li>
<p><strong>Contributions to open-source cheminformatics or experience evaluating third-party chemistry platforms.</strong></p>
</li>
<li>
<p><strong>Experience building and optimizing Model Context Protocols (MCPs), agentic frameworks, or integrating cheminformatics tools with LLM-based workflows.</strong></p>
</li>
</ul>
<h2><strong>Why Join Ginkgo Datapoints?</strong></h2>
<ul>
<li>
<p><strong>Build cheminformatics technology and workflows on Nebula, the world’s largest autonomous laboratory.</strong></p>
</li>
<li>
<p><strong>Help connect molecular design, reaction prediction, synthesis, testing, and learning across Ginkgo’s automated platforms.</strong></p>
</li>
<li>
<p><strong>Develop workflows to ingest, structure, and learn from the high-quality data generated by autonomous laboratory systems.</strong></p>
</li>
<li>
<p><strong>Join a technically ambitious, cross-functional team building the computational foundations for AI-enabled drug discovery.</strong></p>
</li>
<li>
<p><strong>Receive competitive compensation, generous equity in a public company, and a robust benefits package.</strong></p>
</li>
</ul>
<p></p>
<p> </p>
<hr />
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<p><em>The base salary range for this role is $115,00-165,000. Actual pay within this range will depend on a candidate's skills, expertise, and experience. We also offer company stock awards, a comprehensive benefits package including medical, dental &amp; vision coverage, health spending accounts, voluntary benefits, leave of absence policies, 401(k) program with employer contribution, 8 paid holidays in addition to a full-week winter shutdown and unlimited Paid Time Off policy.</em></p>
</div>
<div>
<p><strong>Ginkgo has implemented a return to office policy effective October 1, 2025 with required in office days 5x per week. This policy applies to all employees who live within 50 miles of Ginkgo’s offices in Boston, MA, Emeryville, CA and West Sacramento, CA.</strong></p>
</div>
</div>
</div>
</div>
</div>
</div>
</div><div><div> </div>
<div>It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees, employment applicants, and EOE disability/vet. </div>
<div> </div>
<div><hr /></div>
<div> </div>
<div><span><strong>Privacy Notice</strong></span></div>
<div>I understand that I am applying for employment with Ginkgo Bioworks and am being asked to provide information in connection with my application. I further understand that Ginkgo gathers this information through a third-party service provider and that Ginkgo may also use other service providers to assist in the application process.  Ginkgo may share my information with such third-party service providers in connection with my application and for the start of employment.  Ginkgo will treat my information in accordance with Ginkgo's <a href="https://www.ginkgobioworks.com/concentric-privacy-policy/" target="_blank" rel="noopener noreferrer ugc">Privacy Policy</a>.  By submitting this job application, I am acknowledging that I have reviewed and agree to Ginkgo's <a href="https://www.ginkgobioworks.com/concentric-privacy-policy/" target="_blank" rel="noopener noreferrer ugc">Privacy Policy</a> as well as the privacy policies of the third-party service providers used by Ginkgo's associated with the application process.</div></div>

Ginkgo Bioworks

Open jobs at Ginkgo Bioworks

Scientist, AI/ML — Antibody Developability

Cheminformatics & Synthesis Prediction

Working for Ginkgo Bioworks?

Featured companies