Senior Machine Learning Scientist

Position Overview

Adaptive Biotechnologies is seeking a Senior Machine Learning Scientist to contribute to the development of deep learning models for TCR–pMHC specificity prediction. In this role, you will design, implement, train, and evaluate machine learning models that predict interactions between T cell receptors and peptide–MHC complexes, with a focus on integrating sequence and structural information.

Working as part of a collaborative modeling team, you will implement new modeling ideas, rigorously test their performance, and iterate quickly to improve predictive accuracy. This work leverages large-scale proprietary immune receptor datasets and shared GPU infrastructure to support rapid experimentation and model development.

You will work closely with computational scientists, immunologists, and machine learning engineers across the organization. The team combines expertise in immune biology, experimental assay development, and large-scale machine learning, enabling a tight feedback loop between model development and experimental data generation. Model outputs often highlight gaps in available data or suggest new experimental directions, while newly generated datasets provide additional signal for improving and validating predictive models.

Models developed in this role contribute directly to diagnostic and therapeutic initiatives both within Adaptive and through external partnerships. This role is well suited for a hands-on scientist who thrives in a collaborative environment and is motivated to translate cutting-edge machine learning into meaningful biological and clinical impact.

Key Responsibilities and Essential Functions

• Design, implement, train, and iterate on novel deep learning models 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.
• Utilize scalable training infrastructure to support large-scale model development and experimentation.
• Conduct 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.
• Provide input and technical recommendations to broader modeling discussions and roadmap planning.
• Work closely with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, and aligned with overall product goals.
• Communicate modeling insights, approaches, and results to cross-functional scientific audiences.
• Contribute to publications, presentations, etc. through technical execution and analysis.

Position Requirements

Required

• PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science) + 5 years progressive experience applying machine learning to real-world scientific or biological problems OR equivalent combination of education and experience.
• Progressive experience in the development and deployment of deep learning methods
• Strong hands-on experience in python and modern ML tooling (PyTorch preferred)
• Strong experience in 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, with ability to present highly technical material to diverse audiences.
• Demonstrated ability to collaborate effectively in cross-functional, multi-disciplinary 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:
• • Demonstrated track record of implementing novel machine learning solutions to biological problems, as demonstrated by publications, conference papers, patents, or delivered products.
  • Protein structure prediction
  • Protein design and generative modeling
  • Protein language models
  • Large-scale biological datasets
  • Immunology and immune receptor specificity (antibody or TCR)

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Compensation

Salary Range: $144,600 - $217,000

Other compensation elements include:

• equity grant
• bonus eligible