Senior 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 Senior Machine Learning Scientist to contribute to the development of models for TCR–pMHC specificity prediction. In this role, you will design, implement, train, and evaluate 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-ed