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BigHat Biosciences
BigHat Biosciences
51 – 200 Employees
ConsultingHealthcareManufacturing
BigHat Biosciences is a biotechnology company developing antibody therapies for challenging diseases, including infections and cancers. Its Milliner platform combines machine learning with a high-speed wet lab to design and engineer antibodies, bringing computational models into an experimental discovery process. The company focuses on improving the safety and effectiveness of potential therapies and advances its programs through research collaborations and partnerships.
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Director of Machine Learning, Antibody Design

Lead machine learning strategy and antibody-design modeling at BigHat Biosciences, bringing together frontier models, laboratory data, and LLM automation. Guide a senior interdisciplinary team and help improve design cycles supporting more than 2,000 antibodies each week.

USUnited StatesCACalifornia
$250,000 – $365,000 / year
HybridFull-timeLeadEnglish RequiredMachine Learning Engineer
CloudPythonPyTorch

Description

  • Set and refine BigHat’s machine learning roadmap.
  • Define the future direction of machine learning and advance BigHat’s leadership in the field.
  • Build multi-objective predictive models using BigHat’s data repository and data-generation capabilities.
  • Develop and train models for complex, multispecific antibody design.
  • Apply frontier models to protein-design challenges using BigHat’s datasets.
  • Connect laboratory work, computational platforms, data, and LLM automation to improve the design cycle.
  • Guide the unified LLM and machine learning strategy across the CIO organization.
  • Promote safe, effective use of LLMs, agent frameworks, and reinforcement-learning evaluation methods.
  • Ensure model provenance and benchmarks are reliably carried through infrastructure.
  • Ensure design rounds use model insights across workflows designing more than 2,000 antibodies per week.
  • Provide computational analyses, predictions, and optimized variant sets for internal and partner programs.
  • Turn data-generation and modeling capabilities into offerings for frontier AI companies and biopharma partners.
  • Define pilot scope and technical success criteria, then deliver the pilots.
  • Mentor and develop a senior team of machine learning scientists and engineers.
  • Publish, present, and participate in machine learning and protein-engineering communities.

Requirements

  • Ph.D. in machine learning, computer science, computational biology, or a related field.
  • More than eight years of experience developing and applying advanced machine learning methods.
  • Industry experience is strongly preferred.
  • Demonstrated ability to lead technical teams or major technical programs.
  • Deep, current expertise in modern machine learning, including generative models of protein sequence and structure, Bayesian optimization, active learning, uncertainty quantification, data-efficient predictive modeling, and in-context learning.
  • Strong Python and PyTorch skills.
  • Experience with modern software engineering practices, cloud computing, and MLOps.
  • Experience deploying machine learning in production systems.
  • Knowledge of reproducibility, provenance, benchmarking, and cycle time.
  • Working knowledge of antibody biology, developability, and drug development, or the ability and willingness to learn these quickly.
  • Familiarity with display, NGS, and high-throughput biophysical data is a strong advantage.
  • Excellent written and verbal communication skills.
  • Ability to work effectively across disciplines with protein scientists, automation engineers, software engineers, and drug developers.
  • Credibility with partners and external audiences.
  • Motivation by BigHat’s mission and potential impact.

Benefits

  • Bonus
  • Stock options
  • Benefits

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