Jobs / United States / Ginkgo Bioworks INC
Scientist, AI/ML — Antibody Developability
Ginkgo Bioworks INC · 🇺🇸 California, United States
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 6 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 5 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Ginkgo Bioworks INC paid sponsored hires in similar roles1 certified filing for “Senior Scientist, Antibody Discovery & Characterization” (Biochemists and Biophysicists) in MA: $144k–$144k, median $144k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Ginkgo Bioworks INC
The US Department of Labor certified 6 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 5 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Ginkgo Bioworks INC →
About the 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. 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). The base salary range for this role is $130,600 – $183,800. Actual pay within this range will depend on a candidate's skills, expertise, and experience. We also offer company stock awards, a comprehensive benefits package including medical, dental & vision coverage, health spending accounts, voluntary benefits, leave of absence policies, 401(k) program with employer contribution, 8 paid holidays in addition to a full-w