<div class="content-intro"><p><strong>At Adaptive, we’re Powering the Age of Immune Medicine. </strong>Our goal is to harness the power of the adaptive immune system to transform the way diseases are diagnosed and treated.</p> <p>As an Adapter, you’ll have the opportunity to make a difference in people’s lives. With Adaptive, you’ll create a <span style="text-decoration: underline;">career highlight</span> through collaboration with bright, curious colleagues working at the apex of innovation and application.</p> <p>It’s time for your next chapter. Discover your story with Adaptive.<br><br></p></div><p><strong>Senior </strong>or <strong>Principal Machine Learning Scientist</strong></p> <p><strong>Position Overview</strong></p> <p>Adaptive Biotechnologies is seeking a <strong>Senior </strong>or <strong>Principal Machine Learning Scientist</strong> 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.</p> <p>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.</p> <p>At the <strong>Senior</strong> level, the role emphasizes independent ownership of major modeling workstreams, rigorous technical execution, and strong cross-functional collaboration.</p> <p>At the <strong>Principal</strong> level, the role expands to broader technical leadership, modeling and data strategy, roadmap influence, and long-term direction setting across teams.</p> <p><strong>Key Responsibilities and Essential Functions</strong></p> <ul> <li>Design, implement, train, and evaluate deep learning models for TCR-pMHC specificity prediction.</li> <li>Integrate sequence, structural, and large-scale biological data into principled modeling approaches.</li> <li>Adapt advances in protein structure prediction, protein-protein interaction modeling, protein language models, representation learning, and related areas to immune receptor prediction.</li> <li>Develop rigorous benchmarking and evaluation strategies to assess model performance, generalization, and practical utility.</li> <li>Translate biological principles of T cell recognition into modeling decisions.</li> <li>Use model results to inform experimental data generation and identify opportunities to improve model performance.</li> <li>Partner with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, scalable, and aligned with product goals.</li> <li>Communicate modeling approaches, results, and limitations to scientific and technical audiences.</li> <li>Contribute to publications, presentations, intellectual property, and internal technical decision-making.</li> <li>Perform all other duties as assigned.</li> </ul> <p><strong>Additional Expectations at the Principal Level</strong></p> <ul> <li>Define modeling and data strategy across multiple workstreams or programs.</li> <li>Evaluate emerging machine learning advances and determine how they should shape Adaptive’s modeling roadmap.</li> <li>Set long-term technical direction for machine learning approaches in immune receptor specificity prediction.</li> <li>Influence organization-wide standards for model evaluation, benchmarking, reproducibility, and scientific rigor.</li> <li>Communicate complex modeling insights to scientific leadership, executives, and external partners.</li> <li>Mentor and influence scientists and engineers through technical leadership and strategic judgment.</li> </ul> <p><strong>Position Requirements</strong></p> <p><strong>Required Qualifications for Both Levels</strong></p> <ul> <li>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.</li> <li>Progressive experience developing and deploying deep learning methods for scientific, biological, or similarly complex real-world problems.</li> <li>Strong hands-on experience with Python and modern machine learning tooling; PyTorch preferred.</li> <li>Strong experience with deep learning architecture design and implementation.</li> <li>Experience working with large datasets and high-performance computing environments.</li> <li>Ability to independently define, scope, and execute complex technical research problems.</li> <li>Strong written and verbal communication skills, including the ability to present technical material to diverse audiences.</li> <li>Demonstrated ability to collaborate effectively in cross-functional, multidisciplinary teams.</li> <li>Motivation to see models transition from research into clinical and commercial application.</li> </ul> <p><strong>Level-Specific Requirements</strong></p> <p><strong>Senior Machine Learning Scientist</strong></p> <