Scientist I, Computational Protein Design

Generate Biomedicines•Somerville, MA

7d•$140,000 - $196,000•Hybrid

About The Position

Generate Biomedicines is seeking a Computational Protein Design Scientist to work at the intersection of computational protein design, machine learning, and experimental biology. The role uses computational expertise and biochemical understanding to guide protein design strategy for therapeutic programs and to develop reusable capabilities for new design challenges. It connects machine learning and experimental platform teams to integrate AI-designed proteins with experimental data. The Scientist combines hands-on protein design with computational experimentation and collaboration across program, experimental, and computational teams. The role translates therapeutic hypotheses into design and data-generation plans, executes design campaigns, interprets results, and helps guide decisions in cross-functional teams. It also turns application learning—including recurring needs, successful approaches, and failure modes—into methods, workflows, and scientific requirements that improve program execution. The position is based in the Somerville, Massachusetts office, with flexibility for hybrid work. The stated base salary range is $140,000–$196,000 per year. The role is also eligible for an annual bonus, equity compensation, and a competitive benefits package.

Requirements

PhD in protein engineering, biochemistry, bioengineering, molecular biology, computational biology, bioinformatics, or a related field.
Demonstrated experience formulating protein design strategy in areas such as de novo protein design, protein optimization, protein engineering, antibody engineering, or related molecular-design problems.
Strong practical understanding of protein sequence, structure, and their relationship to molecular properties and biological function.
Experience with experimental design and interpreting complex biological datasets to guide protein design decisions.
Working proficiency in Python and ability to develop reproducible analyses and computational workflows.
Strong scientific judgment and creativity, with the ability to propose and act on decisions with incomplete information.
Ability to communicate technical concepts, assumptions, risks, and tradeoffs clearly to cross-functional audiences.

Nice To Haves

Experience with machine learning, statistical modeling, developing AI-agent workflows, workflow orchestration, or programmatic interfaces; these capabilities can also be developed in the role.

Responsibilities

Translate therapeutic hypotheses and program needs into end-to-end protein design campaigns, data strategies, and technical milestones, while identifying and navigating key uncertainties.
Identify recurring capability gaps, successful approaches, and failure modes, and translate them into improved methods, workflows, and priorities.
Connect computational model development with experimental validation, sharing biological and computational learnings between teams.
Use biochemical understanding to shape informative experiments, considering complex assay readouts, signal and noise properties, and experimental-design tradeoffs.
Contribute to workflows that connect scientific information, protein-design models, data, and tools; apply them to program questions and design decisions and evaluate their outputs and reliability.
Develop and communicate data-driven design recommendations that connect molecular observations to program context and next-step decisions.
Document assumptions, results, and limitations so work is understandable and reusable.