Applied Machine Learning/AI Scientist

Role Overview

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).