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(TM) platform—which maps the immune synapse between T cell receptors (TCRs) and their antigen targets—Repertoire translates unique biological insights into potent, targeted, off‑the‑shelf immune medicines. The company combines deep protein engineering expertise with artificial intelligence, powered by a DECODE database of over one billion TCR‑antigen interactions, to accelerate discovery and optimise drug candidates. From its sites in Cambridge, Massachusetts and Zurich, Switzerland, Repertoire is advancing a pipeline of T cell‑targeted immunotherapies that can address a broad range of cancers and autoimmune disorders. The lead oncology program, RPTR‑1‑201, a TCR bispecific, has initiated a Phase 1/2 clinical trial across multiple solid tumour indications. Repertoire plans to advance additional TCR bispecific therapies into clinical trials over the next 12–18 months. In autoimmune disease, the company partners with leading pharmaceutical firms to develop mRNA‑tolerising therapies that selectively expand regulatory T cells and reset the immune system. Role Overview 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 modelling and deep learning—to better understand the immune response and leverage these insights to develop transformational immune medicines. The candidate will collaborate closely with experimental, clinical and computational colleagues to translate computational insights into therapeutic candidates and biomarker strategies. Key Responsibilities Assist in the conception, development, optimisation and evaluation of machine learning models to better understand the TCR–peptide‑MHC interface. Develop, evaluate and implement rigorous analytical models and methods 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 optimisation and biomarker development. Maintain familiarity with current scientific literature to assist in the development and benchmarking of new methods. Communicate findings internally and externally via presentations and publication. Qualifications / Experience 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 modelling or related ML approaches for biological data. Familiarity with evaluating and interpreting predicted protein structures, including interface confidence metrics (pTM, ipTM), and incorporating structural features into machine learning workflows. Strong programming skills in Python, including experience with scientific and ML libraries such as NumPy, SciPy, pandas, PyTorch and/or TensorFlow. Proven ability to analyse 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 (preprints, conference papers, internal technical reports). Intellectual curiosity, scientific rigor and enthusiasm for working in a fast‑paced, evolving research environment. Preferred Qualifications Experience working with TCR‑pMHC binding is a strong plus as well as a background in immunology / immuno‑oncology. Practical experience with PLM fine‑tuning, embedding extraction and attention‑based interpretation for downstream biological tasks (binding prediction, fitness landscapes, mutational scanning). Experience with structural modelling tools and frameworks, including AlphaFold, RoseTTAFold, ESMFold, ProteinMPNN, RFdiffusion, and graph neural networks operating on 3D protein coordinates. Salary & Benefits The base salary for this role ranges from $134,000 to $160,000 and is determined based on a candidate’s skills, experience and internal equity. In addition to a competitive base salary, Repertoire offers a broad range of benefits, including medical, dental, vision and life insurance, flexible time off, a 401(k) retirement plan and short‑ and long‑term incentive opportunities. Compensation and benefits may be updated in the future. Equal Opportunity / Inclusive Culture Repertoire is committed towards social responsibility and developing an inclusive culture. We recognise that the diversity of T and B cells underpins immune function, and we aim to cultivate a diverse workforce that reflects this principle. Repertoire is proud to be an Equal Opportunity Employer. Recruitment & Staffing Agencies Repertoire Immune Medicines does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Repertoire or its employees is strictly prohibited unless contacted directly by Repertoire’s internal Human Resources team. Any resume submitted by an agency without a signed agreement will automatically become the property of Repertoire, and Repertoire will not owe any referral or other fees for it. #J-18808-Ljbffr Repertoire-Immune-Medicines