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. 

Scientist, AI/ML — Antibody Developability

Datapoints Team | Ginkgo Bioworks | Boston, MA

About the Role

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.

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.

Key Responsibilities

• Develop and validate predictive models for developability using PLM embeddings, structural features, and physicochemical descriptors.
• Implement leakage-aware evaluation frameworks for rigorous performance assessment of small-scale datasets.
• Evaluate cross-format transferability (IgG to VHH-Fc, bispecifics) and deploy data-efficient training strategies.
• Perform rigorous QC on biophysical assay data to identify outliers and maintain high data integrity for modeling.
• Maintain and develop automated pipelines to ensure reproducibility and facilitate technical reporting.
• Disseminate findings through technical reviews, peer-reviewed manuscripts, and external scientific presentations.

Qualifications

Required

• Ph.D. in Computational Biology, Bioinformatics, ML, Biophysics, or related quantitative field with emphasis on applied ML.
• Proficiency in Python and the scientific computing stack (NumPy, pandas, PyTorch/TensorFlow).
• Demonstrated experience in supervised learning on biological datasets, including expertise in cross-validation and bias mitigation.
• Knowledge of protein representations, including PLM embeddings (e.g., ESM, AbLang) and structural featurization.
• Ability to analyze biophysical data with respect to signal-to-noise ratios and experimental dynamic range.
• Effective communication across multidisciplinary teams and ability to manage concurrent technical objectives.

Preferred

• Familiarity with antibody formats (VHH, scFv), therapeutic developability liabilities, and relevant characterization assays.
• Experience with state of the art supervised techniques such as tabular foundation models.
• Exposure to generative sequence modeling, diffusion models, and reward-based steering.
• Experience with protein structure prediction (AlphaFold, ABodyBuilder) and surface patch analysis.
• Prior engagement with high-throughput experimental design and collaborative data generation.
• Record of scientific publication and software engineering fundamentals (Git, testing, cloud compute).

Location: Boston, MA (Hybrid).