Director of Machine Learning

BigHat Biosciences

. Own and continuously sharpen BigHat's machine learning roadmap .

Posted 10/19/2026full-timeSan Mateo • California • United StatesLead💰 $250,000 - $365,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning methodologies, particularly in generative models, Bayesian optimization, and data-efficient predictive modeling. Proven ability to lead technical teams and integrate computational platforms with laboratory processes to optimize antibody design and development.

Highest-signal resume keywords

Ph.D. In Machine Learning8+ Years Of Experience In ML MethodsStrong Python And PyTorch SkillsLeadership Of Technical TeamsKnowledge Of Antibody Biology

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenerative ModelsBayesian OptimizationActive LearningUncertainty QuantificationData-Efficient Predictive ModelingIn-Context LearningMLOpsComputational AnalysisModel Training

Soft Skills

Excellent Written CommunicationExcellent Verbal CommunicationInterdisciplinary CollaborationMentoring

Tools & Technologies

Cloud ComputingHigh-Throughput Biophysical DataNGSDisplay Technologies

Industry Keywords

Protein DesignAntibody DevelopmentDrug DevelopmentModel ProvenanceBenchmarking

Tech Stack

Tools & technologies

CloudPythonPyTorch

About the role

Key responsibilities & impact

Own and continuously sharpen BigHat's machine learning roadmap
Articulate the future direction of machine learning and BigHat's leadership position
Create multi-objective predictive models using BigHat's data repository and data-generation capabilities
Develop and train models for sophisticated, multi-specific antibody design
Apply frontier models to protein design challenges using BigHat's datasets
Integrate the laboratory, computational platform, data, and LLM automation to optimize the design loop
Steer the unified LLM and ML approach across the CIO organization
Drive adoption of safe, impactful LLMs, agent frameworks, and reinforcement-learning evaluation frameworks
Ensure model provenance and benchmarks flow through infrastructure reliably
Ensure design rounds leverage model insight for more than 2,000 antibodies designed weekly
Deliver computational analyses, predictions, and optimized variant sets for internal and partnered programs
Translate data-generation and modeling capabilities into offerings for frontier AI companies and biopharma partners
Scope pilots, define technical success criteria, and deliver pilots
Mentor and grow a senior team of ML scientists and engineers
Publish, present, and engage with ML and protein-engineering communities

Requirements

What you’ll need

Ph.D. in Machine Learning, Computer Science, Computational Biology or a related field
8+ years of experience developing and applying advanced ML methods
Industry experience strongly preferred
Demonstrated leadership of technical teams or major technical programs
Deep, current command of 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
Modern software engineering practice, cloud computing, and MLOps
Experience shipping ML into production systems
Knowledge of reproducibility, provenance, benchmarking, and cycle time
Working knowledge of antibody biology, developability, and drug development, or ability and appetite to acquire it quickly
Familiarity with display, NGS, and high-throughput biophysical data is a strong plus
Excellent written and verbal communication
Ability to thrive in interdisciplinary settings alongside protein scientists, automation engineers, software engineers, and drug developers
Credibility with partners and external audiences
Motivation by BigHat's mission and impact

Benefits

Comp & perks

Bonus
Stock options
Benefits