Deep Learning Scientist II, Protein Sciences

Repertoire Immune Medicines•Watertown, MA

About The Position

Repertoire Immune Medicines is a clinical-stage biotechnology company harnessing the power of the human immune system to develop transformative therapies for cancer and autoimmune disease. Using its proprietary DECODE TM platform—which maps the immune synapse between T cell receptors (TCRs) and their antigen targets—Repertoire translates unique biological insights into potent and targeted off-the shelf immune medicines. The company integrates deep protein engineering expertise with artificial intelligence, powered by a proprietary DECODE database of over one billion TCR-antigen interactions, to accelerate discovery and optimize drug candidates. From its sites in Cambridge, Massachusetts and Zurich, Switzerland, Repertoire is advancing a pipeline of T cell-targeted immunotherapies with the potential to address a broad range of cancers and autoimmune disorders. The company’s lead oncology program, RPTR-1-201, a TCR bispecific, has initiated a Phase 1/2 clinical trial across multiple solid tumor indications. Repertoire plans to advance additional TCR bispecific therapies into clinical trials over the next 12-18 months. In autoimmune disease, Repertoire is partnering with leading pharmaceutical companies to develop mRNA tolerizing therapies designed to selectively expand regulatory T cells and reset the immune system. Repertoire was founded in 2019 by Flagship Pioneering and is supported by a strong investor base. The DECODE platform has been validated through four strategic partnerships with leading pharmaceutical companies—Bristol Myers Squibb, Genentech, Eli Lilly, and Pfizer—representing over $4.5 billion in disclosed total deal value and $185 million in upfront payments received to date. 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.

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.