Tech Stack

Description

You will lead the development of deep learning models for TCR–pMHC specificity prediction at Adaptive Biotechnologies, leveraging proprietary datasets to design and train models that integrate sequence and structural information. Your work will directly support diagnostic and therapeutic initiatives, collaborating with computational scientists and immunologists to translate advances from research into clinical and commercial applications.

Requirements

• PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science) + 12 years progressive experience in machine learning, applied statistics or related field in a life sciences or biotech environment
• Masters + 15 years, 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
• 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
• 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
• Preferred: Experience in protein structure prediction, protein design and generative modeling, protein language models and large-scale foundation models, working with large-scale biological datasets, immunology and immune receptor specificity

Responsibilities

• 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
• Define modeling and data strategy, translating biological principles 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