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About the Company We envision a world where we continuously, sustainably, and affordably improve human life with synthetic biology. Our proprietary set of enzymes and our manufacturing approach enables the production of thousands of amino acids that were previously too difficult and expensive to make. These “noncanonical” amino acids that we make are already catalyzing the creation of revolutionary and innovative products that are good for people and the planet.  Our technology outperforms traditional manufacturing approaches by 10–100x across the board. We already sell products to Big 10 Pharmaceutical companies, and our product family includes the key components of multi-billion dollar blockbuster drugs such as Ozempic and Mounjaro. We are a diverse, passionate, interdisciplinary team that finds joy in building something new that leaves the world better than we found it. We strive to always learn and improve and have a deep desire to capitalize on our creativity and be exceptional at what we do. About the role We're looking for a Data Scientist to help build the machine learning capabilities that will power the next phase of our R&D platform. In this role, you'll report to the Director of R&D and work side-by-side with laboratory scientists, applying machine learning and chemoinformatics to turn experimental data into testable predictions that accelerate scientific discovery. You'll have the opportunity to own models from initial concept through deployment, influence the technical direction of our ML infrastructure, and build scalable data pipelines that become foundational to the team's work.  If you're excited by applying cutting-edge machine learning to real-world scientific problems in a highly collaborative, interdisciplinary environment, this is a chance to have a meaningful impact on both our research strategy and the company's future growth. This is a full-time, on-site role based out of Berkeley, CA. What You’ll Do Design, build, and deploy machine learning models to predict chemical properties and support de novo design of small molecules and peptides, taking models from concept through validation and production.  Own the development and maintenance of scalable data pipelines that ingest, curate, and prepare experimental screening data for machine learning applications, ensuring reproducibility and reliability.  Partner closely with laboratory scientists to translate experimental questions into machine learning approaches, generate actionable predictions, and prioritize new compounds or target areas for validation.  Develop and improve the team's machine learning infrastructure , including model training, evaluation, retraining, monitoring, and continuous improvement as new experimental data becomes available.  Apply chemoinformatics tools and molecular representations to engineer features, evaluate model performance, and improve predictive accuracy across R&D workflows.  Communicate insights and recommendations through clear visualizations and presentations, helping both technical and non-technical stakeholders understand model performance and scientific findings.  Contribute to the technical direction of the machine learning platform , collaborating with scientists and other technical team members to identify opportunities for new models, datasets, and workflows that accelerate research. What You’ll Bring Ph.D. or Master’s in Computational Chemistry, Chemoinformatics, or a related field and 2-4 years of post-graduate experience, with a strong foundation in chemical structure representation and molecular property prediction. Proven track record in building and deploying machine learning (ML) and deep learning models to predict chemical characteristics and drive de novo design for chemical compounds with superior properties. Experience working with small molecule or peptide datasets. High