Applied Machine Learning/AI Scientist

Repertoire Immune Medicines

Cambridge, MA

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JOB DETAILS

JOB TYPE

Full-time

SKILLS

Alliance/Partner Management, Analysis Skills, Artificial Intelligence (AI), Autoimmune Disease, Benchmarking, Biology, Biomarkers, Biomedical Engineering, Biostatistics, Biotech and Pharmaceutical, Cancer, Candidate Sourcing, Clinical Trial, Communication Skills, Computer Programming, Computer Science, Cross-Functional, Data Science, Deep Learning, Diversity, Drug Development, Drug Discovery, Fitness, Genetics, Human Resources, Immunology, Laboratory, Machine Learning, Medical Products, Metrics, Modeling Languages, Neural Networks, Oncology, Phase I Clinical Trials, Phase II Clinical Trials, Product Lifecycle Management, Project/Program Management, Publications, Python Programming/Scripting Language, Recruiting/Staffing Agency, Regulations, Scientific Publications, Statistics, Technical Publications, Training Data Sets

LOCATION

Cambridge, MA

POSTED

Today

Applied Machine Learning/AI Scientist

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.

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 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.

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).

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 designed to attract, retain, and motivate top talent, 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 are based on Repertoire’s good faith estimate at the time of publication and may be updated in the future.

Repertoire is committed towards social responsibility and developing an inclusive culture. Much as the power of the immune system lies in the diversity of T and B cells, we believe that our work requires the creativity and ingenuity of a diverse workforce, and we are committed to pursuing that in all facets of the work experience at Repertoire. We will continue to educate ourselves about the inequities and barriers present in our society and act as a company where we can make a difference.
Repertoire is proud to be an Equal Opportunity Employer.

Recruitment & Staffing Agencies: Repertoire Immune Medicines (“Repertoire”) 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 in the absence of a signed agreement will automatically become the property of Repertoire, and Repertoire will not owe any referral or other fees with respect thereto.

About the Company

Repertoire Immune Medicines

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