Scientist, AI/ML — Antibody Developability

The role

You will build and benchmark machine learning models to predict and design antibody developability. You will work with the PROPHET-Ab biophysical platform and various datasets including customer data and the GDPa public dataset series.

What you will do

• 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 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.

Required qualifications

• 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.

Nice to have

• 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).

Benefits

• Company stock awards
• Medical, dental & vision coverage
• Health spending accounts
• Voluntary benefits
• Leave of absence policies
• 401(k) program with employer contribution
• 8 paid holidays
• Full-week winter shutdown
• Unlimited Paid Time Off policy

About the company

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. Their bioengineers make use of an in-house automated foundry for designing and building new organisms.