Applied Machine Learning​/AI Scientist

Applied Machine Learning/AI Scientist

Repertoire Immune Medicines is a clinical‑stage biotechnology company that harnesses the power of the human immune system to develop transformative therapies for cancer and autoimmune disease. Using its proprietary DECODE 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 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 Immune Medicines is seeking an Applied Machine Learning Scientist to join the Artificial Immune Intelligence team to enable the discovery of new insights from our extensive and growing immune synapse database. The successful candidate will work at the intersection of applied machine learning, statistics, computational biology, and data science with broad impact across early discovery, candidate development, and biomarker discovery efforts.

This position offers a unique opportunity to apply and advance state‑of‑the‑art computational methods—including protein language models, structural modeling, and deep learning—to better understand the immune response and leverage these insights to develop transformational immune medicines. The successful candidate will collaborate closely with experimental, clinical, and computational colleagues to translate computational insights into therapeutic candidates and biomarker strategies.

• Assist in the conception, development, optimization, and evaluation of machine learning models to better understand the TCR–peptide‑MHC interface.
• Develop, evaluate, and implement rigorous analytical models and methods as needed for scientific discovery and development.
• Work alongside other machine learning scientists, computer science engineers, wet‑lab scientists, and project managers, contributing to early discovery, lead identification, lead optimization, and biomarker development.
• Maintain familiarity with current scientific literature to assist in the development and benchmarking of new methods.
• Communicate findings both internally and externally via presentations and publication.

• PhD in computational biology, machine learning, engineering, statistics, biostatistics, biomedical engineering, immunology, genetics, cancer biology, or a related quantitative field; or a Master’s degree with 3+ years of relevant industry or academic experience.
• Demonstrated ability to deliver impact in cross‑functional, multidisciplinary scientific teams.
• Hands‑on experience with protein language models (PLMs), structural modeling, or related ML approaches for biological data.
• Familiarity with evaluating and interpreting predicted protein structures, including interface confidence metrics (e.g., pTM, ipTM), and incorporating structural features into machine learning workflows.
• Strong programming skills in Python, including experience with scientific and ML libraries such as Num Py, Sci Py, pandas, PyTorch, and/or Tensor Flow.
• Proven ability to analyze and model complex, high‑dimensional biological datasets using sound computational and statistical practices to drive novel insights.
• Track record of contributing to scientific publications or equivalent technical outputs (e.g., preprints,…