Principal Scientist, Computational Protein Design
$142,500–$256,500 year
On-siteCambridge, Massachusetts, United States or Research, Victoria, Australia
Job Summary
Lead and execute computational protein design campaigns focused on de novo binder generation for T cell receptors and antibodies, optimizing affinity, specificity, and developability. Drive computational optimization approaches to improve potency and compatibility with mRNA-expressed formats while establishing integrated design platforms connecting generative modeling, docking, and active-learning cycles. Implement off-target screening strategies for engineered binders and partner with structural biology, directed evolution, and program teams to convert model outputs into experimental data. Evaluate and deploy state-of-the-art methods including structure prediction, inverse folding, and protein language models to advance discovery programs across Moderna's therapeutic portfolio.
Required Qualifications
- PhD in computational biology, protein engineering, bioinformatics, structural biology, biophysics, or a closely related discipline
- At least 5 years of post-graduate experience in computational protein design
- Demonstrated track record in de novo binder generation and optimization using AI/ML-guided and structure-informed design approaches
- Demonstrated experience connecting computational design strategies to experimental validation and optimization workflows
- Experience leading or contributing to discovery programs that require alignment across computational biology, AI/ML, data engineering, structural biology, immunology, translational science, preclinical development, external partners, and senior scientific stakeholders
- Strong publication record or equivalent record of scientific impact in computational protein design, structural biology, protein engineering, or a related field
- Technical fluency with modern computational protein design and structural modeling tools, such as AlphaFold, AlphaFold-Multimer, Rosetta, PyRosetta, RFdiffusion, ProteinMPNN, and related models
- Experience mentoring scientists, managing at least one direct report, or providing scientific leadership in a dynamic research environment
- Excellent written, presentation, and interpersonal communication skills, including the ability to translate complex technical tradeoffs into clear recommendations for scientific and executive audiences
- Ability to access export-controlled information in accordance with U.S. law
- Qualify as U.S. persons (citizens, permanent residents, asylees, or refugees)
Desired Qualifications
- Over 5 years of post-graduate experience in computational protein design, with a strong record of de novo binder generation and optimization and peer reviewed publications
- Direct experience with TCR design or TCR therapeutic engineering
- Strong understanding of TCR structure and function, HLA restriction, cross-reactivity, alloreactivity, and the safety constraints involved in engineering therapeutic TCRs
- Experience implementing modern machine learning methods and innovative software workflows to keep pace with advances in computational protein design
- Ability to work directly with computational scientists and AI/ML teams using Python-based workflows and modern machine learning infrastructure
- Experience building computational specificity, developability, and off-target screening workflows
- Familiarity with experimental validation methods that support protein or TCR design, such as yeast, mammalian, or phage display; deep mutational scanning; tetramer or multimer binding assays; developability assays; and cell-based functional assays
- Ability to evaluate protein design opportunities beyond binder discovery, including enzymes or catalysts, DNA- or RNA-binding proteins, CAR signaling domains, engineered scaffolds, switches, sensors, or intracellular functional proteins
- Demonstrated ability to operate as both a technical individual contributor and a cross-functional scientific partner in a fast-paced, program-driven environment
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