Design, implement, and train novel deep learning architectures for TCR–pMHC specificity prediction.
Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting.
Leverage and influence scalable training infrastructure to support large-scale model development and experimentation.
Lead rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior.
Translate biological principles of T cell recognition into principled modeling decisions.
Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains.
Evaluate emerging ML advances and determine when and how to incorporate them into Adaptive’s modeling roadmap.
Shape the long-term technical direction of machine learning in immune receptor prediction across the organization.
Partner with computational biology, immunology, translational, and engineering teams to ensure models are capable, scalable, reproducible, and aligned with therapeutic and diagnostic goals.
Clearly communicate complex modeling insights to scientific leadership, executives, and external partners.
Contribute to intellectual property development and high-impact publications.

Requirements

PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science, etc.) + 12 years progressive experience in machine learning, applied statistics or related field in a life sciences or biotech environment or similar combination of education and experience; an advanced degree preferred.
Masters + 15 years of progressive experience, or
Bachelors + 17 years of progressive experience
Progressive experience in the conception, development and deployment of deep learning methods including substantial hands-on model development
Demonstrated track record of innovating and implementing novel machine learning solutions to biological problems, as demonstrated by publications, conference papers, patents, or delivered products.
Deep expertise in python and modern ML tooling (PyTorch preferred)
Experience with version control and ML experiment tracking
Proven ability to independently define, scope, and execute complex technical research problems.
Excellent written and verbal communication skills, with ability to present highly technical material to diverse audiences.
Exceptional depth in deep learning architecture design and implementation.
Comfortable operating at the frontier of both ML and immunology.
Strategic thinker capable of influencing technical direction across teams.
Driven by impact: motivated to see models transition from research to clinical and commercial application.

Benefits

equity grant
bonus eligible

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

deep learningmachine learningapplied statisticsmodel developmentprotein language modelsstructure predictiongenerative modelingrepresentation learningTCR–pMHC specificity predictionbiological modeling

Soft Skills

excellent communication skillsstrategic thinkingindependent problem solvingcollaborationinfluencepresentation skillsinnovationimpact-drivenleadershipevaluation strategies

Certifications

PhD in quantitative disciplineMasters in relevant fieldBachelors in relevant field