The Role
The Applied Machine Learning Scientist will develop and optimize machine learning models to analyze TCR-peptide-MHC interactions, collaborating with interdisciplinary teams on therapeutic candidate discovery.
Summary Generated by Built In
Applied Machine Learning/AI ScientistRepertoire 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 DECODETM 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 OverviewRepertoire 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
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.
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.
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 OverviewRepertoire 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.
- 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.
- 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.
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.
Skills Required
- PhD in computational biology, machine learning, engineering, statistics, or related field
- Hands-on experience with protein language models and related ML approaches
- Strong programming skills in Python with experience in ML libraries
- Proven ability to analyze complex biological datasets using computational practices
- Track record of contributing to scientific publications
- Familiarity with evaluating predicted protein structures
- Experience with TCR-pMHC binding
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The Company
What We Do
Repertoire Immune Medicines is a biotechnology company dedicated to creating treatments for diseases based on the power of the human T cell repertoire to eliminate cancer cells, target pathogens, and regulate immune function in autoimmune disease. The company’s proprietary DECODE™ platform provides a comprehensive understanding of the interactions between T cell receptors and their antigens in disease. DECODE uniquely elucidates the entire immune synapse, including the T-cell receptor-epitope pairs that defines it, the HLA context, and the T cell phenotype. This capability enables the creation of new and potentially transformative classes of immune-based medicines. Repertoire believes the ability to decipher these interactions represents one of the greatest opportunities for innovation in medical science. The company is utilizing the discoveries from DECODE to design and develop novel immune therapies for multiple therapeutic areas, such as autoimmune diseases, infectious diseases, and cancer. The team operates from sites in Cambridge, Massachusetts and Zurich. To learn more about Repertoire, please visit www.repertoire.com.
