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Ginkgo Bioworks

Platform for cell programming and engineering

Scientist – AI/ML - Antibody Developability

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A Ph.D. in Computational Biology, Bioinformatics, machine learning, Biophysics, or a related quantitative field with emphasis on applied machine learning.
  • Proficiency in Python and the scientific computing stack, including NumPy, pandas, and PyTorch or TensorFlow.
  • Demonstrated experience in supervised learning on biological datasets, including expertise in cross-validation and bias mitigation.
  • Knowledge of protein representations, including protein language model embeddings such as ESM and AbLang, and structural featurization.
  • Ability to analyze biophysical data with respect to signal-to-noise ratios and experimental dynamic range.
  • Ability to communicate across multidisciplinary teams and manage concurrent technical objectives.

Responsibilities

  • Develop and validate predictive models for developability using protein language model embeddings, structural features, and physicochemical descriptors.
  • Implement leakage-aware evaluation frameworks for rigorous performance assessment of small-scale datasets.
  • Evaluate cross-format transferability, including IgG to VHH-Fc and bispecifics, and deploy data-efficient training strategies.
  • Perform quality control 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.

Desired Qualifications

  • Familiarity with antibody formats such as VHH and 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 using AlphaFold or ABodyBuilder, and surface patch analysis.
  • Prior engagement with high-throughput experimental design and collaborative data generation.
  • A record of scientific publication and software engineering fundamentals, including Git, testing, and cloud compute.

About the Company

Ginkgo Bioworks

Platform for cell programming and engineering

Ginkgo Bioworks provides a platform to design, build, and test engineered cells for food, agriculture, pharma, and chemicals using automation and data. Its Foundry automates lab work with robotics and software to execute the Design-Build-Test-Learn loop, while the Codebase is a library of genetic parts and experimental data that guides new projects. The company differentiates itself with an integrated, scalable platform that combines automated wet-lab workflow, a large asset and data library, and AI-enabled design, plus deep partnerships across industries. Its goal is to make biology easier to engineer and speed up the development of biological products through end-to-end tooling and collaborative, data-driven workflows.

Company size
201-500
Company stage
IPO
Headquarters
Boston, MA
Founded
2009

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