Applied Machine Learning/AI Scientist

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.

Key Responsibilities

• 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.

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 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 NumPy, SciPy, pandas, PyTorch, and/or TensorFlow.
• 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, 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 (e.g. binding prediction, fitness landscapes, mutational scanning).
• Experience with structural modeling tools and frameworks, including protein structure prediction (AlphaFold2/3, RoseTTAFold, ESMFold), structure-based design (ProteinMPNN, RFdiffusion), and/or graph neural networks operating on 3D protein coordinates (e.g. GCN, raph Transformers, GVP-GNN).