Senior or Principal Machine Learning Scientist

Senior or Principal Machine Learning Scientist

Position Overview

Adaptive Biotechnologies is seeking a Senior or Principal Machine Learning Scientist to join a team developing deep learning models for TCR-pMHC specificity prediction. This role focuses on predicting interactions between T cell receptors and peptide-MHC complexes by integrating sequence, structural, and large-scale biological data.

In this role, you will work closely with computational scientists, immunologists, translational scientists, and machine learning engineers to build models that support Adaptive’s diagnostic and therapeutic initiatives. The work draws on a large and growing proprietary immune receptor dataset purpose-built for this machine learning problem, as well as shared GPU infrastructure and a close feedback loop between model development and experimental data generation.

At the Senior level, the role emphasizes independent ownership of major modeling workstreams, rigorous technical execution, and strong cross-functional collaboration.

At the Principal level, the role expands to broader technical leadership, modeling and data strategy, roadmap influence, and long-term direction setting across teams.

Key Responsibilities and Essential Functions

• Design, implement, train, and evaluate deep learning models for TCR-pMHC specificity prediction.
• Integrate sequence, structural, and large-scale biological data into principled modeling approaches.
• Adapt advances in protein structure prediction, protein-protein interaction modeling, protein language models, representation learning, and related areas to immune receptor prediction.
• Develop rigorous benchmarking and evaluation strategies to assess model performance, generalization, and practical utility.
• Translate biological principles of T cell recognition into modeling decisions.
• Use model results to inform experimental data generation and identify opportunities to improve model performance.
• Partner with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, scalable, and aligned with product goals.
• Communicate modeling approaches, results, and limitations to scientific and technical audiences.
• Contribute to publications, presentations, intellectual property, and internal technical decision-making.
• Perform all other duties as assigned.

Additional Expectations at the Principal Level

• Define modeling and data strategy across multiple workstreams or programs.
• Evaluate emerging machine learning advances and determine how they should shape Adaptive’s modeling roadmap.
• Set long-term technical direction for machine learning approaches in immune receptor specificity prediction.
• Influence organization-wide standards for model evaluation, benchmarking, reproducibility, and scientific rigor.
• Communicate complex modeling insights to scientific leadership, executives, and external partners.
• Mentor and influence scientists and engineers through technical leadership and strategic judgment.

Position Requirements

Required Qualifications for Both Levels

• PhD in a quantitative discipline such as Machine Learning, Computational Biology, Computer Science, or a related field, or an equivalent combination of education and experience.
• Progressive experience developing and deploying deep learning methods for scientific, biological, or similarly complex real-world problems.
• Strong hands-on experience with Python and modern machine learning tooling; PyTorch preferred.
• Strong experience with deep learning architecture design and implementation.
• Experience working with large datasets and high-performance computing environments.
• Ability to independently define, scope, and execute complex technical research problems.
• Strong written and verbal communication skills, including the ability to present technical material to diverse audiences.
• Demonstrated ability to collaborate effectively in cross-functional, multidisciplinary teams.
• Motivation to see models transition from research into clinical and commercial application.

Level-Specific Requirements

Senior Machine Learning Scientist

• Typically requires PhD plus 5 years of progressive experience applying machine learning to real-world scientific or biological problems, or an equivalent combination of education and experience.
• Strong experience with protein structure prediction or analysis.
• Ability to own substantial modeling workstreams from design through evaluation and iteration.
• Ability to provide technical recommendations that shape team-level modeling plans and priorities.

Principal Machine Learning Scientist

• Typically requires PhD plus 12 years of progressive experience in machine learning, applied statistics, or a related field in a life sciences, biotech, or comparable environment, or an equivalent combination of education and experience.
• Equivalent experience may include:
• Master’s degree plus 15 years of progressive experience.
• Bachelor’s degree plus 17 years of progressive experience.
• Demonstrated track record of innovating and implementing novel machine learning solutions to biological problems, as shown through publications, conference papers, patents, or delivered products.
• Deep expertise in Python and modern machine learning tooling; PyTorch preferred.
• Experience with version control and machine learning experiment tracking.
• Exceptional depth in deep learning architecture design and implementation.
• Comfort operating at the frontier of both machine learning and immunology.
• Strategic judgment and demonstrated ability to influence technical direction across teams.

Preferred Qualifications

Experience in one or more of the following areas is desired:

• Protein structure prediction or analysis.
• Protein-protein interaction modeling.
• Protein design or generative modeling.
• Protein language models or large-scale foundation models.
• Molecular dynamics or free energy perturbation methods.
• Large-scale biological datasets.
• Immunology and immune receptor specificity, including antibody or TCR specificity.
• Publications, conference papers, patents, or delivered products demonstrating applied machine learning impact in biological problems.

Working Conditions

No special working conditions required.

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Compensation

Senior Machine Learning Scientist salary range: $144,600 - $217,000
Principal Machine Learning Scientist salary range: $183,400 - $275,000

Other compensation elements include:

• Equity grant
• Bonus eligibility