Principal Scientist, Computational Protein Design
Principal Scientist, Computational Protein Design
Location: Cambridge, MA (USA)
About the Organization
This role sits within an initiative focused on building next‑generation computational and AI‑driven scientific ventures. The group integrates advances in machine learning, large‑scale data, and molecular science to accelerate early research and create a portfolio of AI‑first biotechnology companies. Operating within a model that blends scientific innovation with entrepreneurial execution and capital, the organization develops breakthrough concepts into transformative companies with applications in human health, sustainability, and other global-impact domains.
The Role
The initiative is pursuing a Principal Scientist specializing in Computational Protein Design to lead cutting‑edge efforts in antibody engineering. This individual will act as the technical driver for computational antibody design strategies-particularly AI‑based and structure‑informed approaches applied to high‑value therapeutic targets.
The role includes close collaboration with a partner drug‑development group that operates a fully integrated biopharmaceutical discovery and development engine built on advanced scientific platforms.
As Principal Scientist, you will guide the development of computational methods for antibody design and optimization, establish foundational workflows for end‑to‑end therapeutic antibody generation, and apply these tools to advance programs within the broader ecosystem. The successful candidate combines deep scientific and computational expertise with strong cross-functional leadership skills and thrives in dynamic, highly integrated research environments.
Key Responsibilities
- Design and optimize antibodies using computational and AI‑driven methods, integrating structure‑based and machine‑learning approaches
- Develop and apply predictive models to evaluate and improve developability traits, including stability, solubility, manufacturability, immunogenicity, polyreactivity, and polyspecificity
- Enhance AI design models using experimental datasets generated across partner teams
- Remain current on emerging computational and AI tools relevant to antibody design
- Provide scientific leadership, mentor teammates, and help guide broader strategy in computational biologics engineering
Professional Experience & Qualifications
- PhD in Biochemistry, Structural Biology, Biophysics, Protein Engineering, Computational Biology, Computer Science, or a related discipline, plus 5+ years of relevant industry experience
- Demonstrated success in designing and optimizing antibodies for binding, biological function, and developability
- Strong background in AI‑enabled and computational techniques relevant to antibody engineering, such as co‑folding models, generative design models, and structure‑based computational tools
- Experience analyzing, interpreting, and applying antibody developability data
- Proven leadership in therapeutic antibody discovery programs or biologics R&D
Preferred Qualifications
- Familiarity with a range of antibody formats, including monoclonal antibodies, bispecific/multispecific modalities, antibody-drug conjugates, and engineered fragments
- Experience building, training, or customizing machine‑learning models for protein engineering, particularly deep learning architectures
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