Principal Machine Learning Scientist

Position Overview

Adaptive Biotechnologies is seeking a Principal Machine Learning Scientist to lead the development of deep learning models for TCR–pMHC specificity prediction.

In this role, you will leverage a large and growing proprietary dataset to design, train, and evaluate models that predict interactions between T cell receptors and peptide–MHC complexes. Your work will focus on developing new approaches that integrate sequence and structural information and rigorously testing their performance in practical settings.

You will work closely with computational scientists, immunologists, and machine learning engineers across the organization. The team brings together expertise in immune biology, experimental assay development, and large-scale machine learning, with access to proprietary immune receptor datasets, shared GPU infrastructure, and engineering support for model development and training.

This role sits at the intersection of modeling and experimental data generation. As model results highlight gaps in available data or suggest new experimental directions, newly generated datasets can provide additional signal for improving and validating the models.

Models developed in this role contribute directly to diagnostic and therapeutic initiatives both within Adaptive and through external partnerships. This position offers the opportunity to advance predictive modeling of immune receptor specificity while seeing those advances translated into meaningful clinical and commercial applications.

Key Responsibilities and Essential Functions

• 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

• 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.

Drive Impact 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.

Position Requirements

Required

• 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.

Preferred

• Experience in one or more of the following areas desired:
  • 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 (antibody or TCR)

Compensation

Salary Range: $183,400 - $275,000

Other compensation elements include:

• equity grant
• bonus eligible

Originally posted on Himalayas