Deep Learning Scientist II, Protein Sciences

Repertoire Immune Medicines•Watertown, MA

50d•$150,000 - $170,000

About The Position

Repertoire Immune Medicines is seeking a Deep Learning Scientist II to join the Artificial Immune Intelligence Team to accelerate the discovery and development of transformative therapeutics, exploiting proprietary datasets about T cell receptors interacting with peptide-MHC complexes. The role involves developing novel deep learning approaches for protein language models, structural modeling, and multi-modal representation learning to analyze the TCR-pMHC synapse. The scientist will apply machine learning, statistics, computational biology, and data science techniques, collaborating with wet-lab scientists. This position requires a PhD in computer science, computational biology, or a related quantitative field, with 2-5 years of industry experience in protein engineering and design. Hands-on experience with training and fine-tuning protein language models and structure-prediction models, and developing applied AI pipelines for biological data is essential. The ability to reason about protein-protein interactions using structure-based approaches, physics-based methods, and machine learning workflows is also required. Strong programming skills in Python, including multi-GPU training with PyTorch, and experience analyzing high-dimensional biological datasets are necessary. Experience with TCR-pMHC binding is a strong plus. The ideal candidate will possess intellectual curiosity, scientific rigor, and enthusiasm for a fast-paced research environment.

Requirements

PhD in computer science, computational biology or a related quantitative field, with 2-5 years of industry experience in protein engineering and design.
Hands‑on experience with training and fine-tuning protein language models (PLMs) and structure-prediction models, and in the development of applied AI pipelines for biological data.
Ability to reason about protein-protein interactions through rational structure-based approaches, using physics-based methods and machine learning workflows.
Strong programming skills in Python, including multi-GPU training with PyTorch and related libraries
Proven ability to analyze and model complex, high‑dimensional biological datasets using sound computational and statistical practices to drive novel insights.
Intellectual curiosity, scientific rigor, and enthusiasm for working in a fast‑paced, evolving research environment.

Nice To Haves

Experience working with TCR-pMHC binding is a strong plus.

Responsibilities

Develop novel deep learning approaches spanning protein language models, structural modeling, and multi-modal representation learning to reason about the TCR-pMHC synapse.
Apply problem-appropriate machine learning, statistics, computational biology, and data science techniques, working closely with wet-lab scientists to understand assay technologies.