Principal Machine Learning Scientist

About this role

At Adaptive, we’re Powering the Age of Immune Medicine. Our goal is to harness the power of the adaptive immune system to transform the way diseases are diagnosed and treated. As an Adapter, you’ll have the opportunity to make a difference in people’s lives. With Adaptive, you’ll create a career highlight through collaboration with bright, curious colleagues working at the apex of innovation and application. It’s time for your next chapter.

Discover your story with Adaptive. 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.