Build autonomous labs to accelerate biotech R&D. Program biology like software. Run experiments on our cloud lab or build your own. Trusted by 130+ customers. Backed by SoftBank VF and Felicis.
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.
What they're looking for
- 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
More about this role
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.
Datapoints Team | Ginkgo Bioworks | Boston, MA
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...
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