Scientist, Protein Design

The Role

Moderna is seeking a talented, creative, and motivated Scientist to contribute to the AI-enabled design of de novo antibodies or other binder modalities and de novo design of antigens across discovery programs. This is a hands-on computational protein design role for a scientist who can apply modern generative methods, agentic AI workflows, and structure-based design tools to help create, score, and improve binders and antigens for experimental validation. The Scientist will be part of a highly collaborative, multidisciplinary team working to invent how AI-designed binders can function in vivo as part of mRNA therapeutics, including as stand-alone biologics, targeting modules, and components of next-generation multi-protein systems.

The Scientist will work closely with computational designers, data scientists, and experimental teams within Computational Design and In Vitro Selection and across Moderna Research to advance novel mRNA-delivered protein candidates. The ideal candidate will bring strong coding and protein-design skills, curiosity about new AI-enabled design approaches, and the ability to translate experimental feedback into improved designs. This is a unique opportunity to work in an innovative environment at the intersection of AI protein design, de novo binder discovery, and mRNA therapeutics.

Here’s What You’ll Do

• Apply AI-enabled protein design workflows to generate and prioritize de novo antibodies and binder modalities or partial de novo design of antigens for experimental testing.
• Use structure prediction, generative design, sequence design, docking, scoring, and developability analyses to create binder designs against diverse therapeutic targets.
• Build, maintain, and improve Python-based workflows for model inference, automated design generation, scoring, filtering, and result visualization under the guidance of senior team members.
• Partner with experimental colleagues to interpret screening, selection, biophysical, and developability data, and use those data to guide iterative design cycles.
• Contribute to benchmarking and validation of new computational methods so that promising tools can be incorporated into binder-design campaigns.
• Explore and prototype agentic AI design workflows that connect target analysis, design generation, scoring, and experimental feedback.
• Develop biological and structural understanding of assigned targets through literature review, analysis of internal and external data, and discussion with project teams.
• Collaborate with cross-functional teammates to plan experiments, summarize design rationale, and communicate results in team meetings and written updates.
• Maintain awareness of advances in generative protein design, agentic AI systems, antibody engineering, and de novo binder discovery.
• Contribute scientifically and technically to multiple discovery efforts in parallel.

Here’s What You’ll Need (Basic Qualifications)

• PhD in Biophysics, Biochemistry, Structural Biology, Computational Biology, Computer Science, or a related field.
• Hands-on experience in computational protein design, protein structural modeling, applied machine learning, or a closely related area.
• Strong Python coding skills and experience building reproducible scientific analysis or design workflows in Linux-based environments.
• Experience with modern protein structure prediction and design tools, such as RFdiffusion, ProteinMPNN, AlphaFold-family methods, Rosetta, protein language models, or related generative approaches.
• Ability to analyze protein designs and experimental datasets, identify next design steps, and communicate conclusions clearly to multidisciplinary collaborators.
• Strong critical-thinking, troubleshooting, and problem-solving skills.
• Excellent written, presentation, and interpersonal communication skills.
• Ability to work both independently and collaboratively in a fast-paced, cross-functional research environment.

Here’s What You’ll Bring to the Table (Preferred Qualifications)

• Experience designing antibody-based biologics or de novo binders.
• Experience developing workflows for HPC or cloud environments, including distributed computing, workflow orchestration, or containerization.
• Experience benchmarking protein design methods, evaluating ML models, or contributing to closed-loop design-test-learn campaigns.
• Experience with agentic AI or LLM-enabled automation for scientific workflows.
• Familiarity with in vitro selection, display technologies, SPR, Octet, developability assessment, or related experimental approaches for binder characterization.
• Track record of publications, presentations, or patents in protein design, structural biology, antibody engineering, or applied m