Principal Scientist, Target Binding

Generate Biomedicines•Somerville, MA

20d

About The Position

Generate Biomedicines is seeking an accomplished Principal Scientist to lead Target Binding efforts within its Protein Analytics & Biophysics department. The position provides scientific and strategic leadership for protein–protein interaction, target-binding, and cell-binding measurements across multiple biotherapeutic discovery and development programs. It combines hands-on technical work, technology development, cross-functional program leadership, and direct management of junior scientists and research associates. The successful candidate will be an established industry expert in quantitative biophysical assay development, particularly for challenging systems such as integral membrane proteins, lipid-associated targets, detergent-solubilized proteins, and multispecific molecules. The role advances the Target Binding technology platform, establishes functional standards, applies quantitative and computational approaches, and communicates scientific findings and recommendations to internal and external stakeholders. Generate Biomedicines is a clinical-stage generative biology company founded in 2018. It integrates machine learning with large-scale experimentation to design proteins with defined biological intent and advance clinical and preclinical programs across disease areas and protein modalities. The company describes its approach as unifying computational design and clinical development to support systematic drug generation.

Requirements

Ph.D. in biochemistry, biophysics, analytical chemistry, protein science, chemical biology, or a related discipline.
At least 8 years of relevant industry experience.
Extensive industry experience developing and applying quantitative protein–protein interaction and target-binding assays.
Deep expertise with multiple interaction-measurement technologies, including SPR, BLI, KinExA, and cell-binding assays.
Strong understanding of associated instrumentation, assay formats, sample-handling requirements, data-processing workflows, and troubleshooting strategies.
Demonstrated success characterizing complex protein–protein interactions.
Track record of developing novel protein-interaction measurement methods or building new technical capabilities.
Experience leading scientific strategy across multiple cross-functional programs.
Proven experience managing, mentoring, and developing scientists or research associates.
Strong quantitative, statistical, communication, and problem-solving skills.

Nice To Haves

Experience with biotherapeutic analytics and developability assessment for antibodies, multispecifics, antibody–drug conjugates, engineered proteins, or other biologic modalities.
Experience with laboratory automation, high-throughput experimentation, instrument integration, or computational data pipelines.
Proficiency with scripting, quantitative modeling, data science, or machine-learning workflows.
Practical experience using AI-enabled or agentic tools for data processing, experiment design, scientific analysis, or workflow automation.
Record of publications, patents, external collaborations, or internally deployed platform technologies.

Responsibilities

Lead Target Binding strategy and execution across multiple biotherapeutic programs, serving as a senior Protein Analytics & Biophysics representative on cross-functional teams.
Design, develop, optimize, and oversee quantitative protein–protein interaction and cell-binding assays.
Develop fit-for-purpose approaches to measure affinity, kinetics, stoichiometry, competition, thermodynamics, and other complex binding mechanisms.
Lead assay development for integral membrane proteins, lipid-raft-associated targets, detergent-solubilized proteins, membrane mimetics, and multispecific molecules.
Advance the Target Binding technology platform by evaluating emerging technologies and developing novel, automated, high-throughput, or computationally enabled measurement approaches.
Establish standards for assay quality, instrumentation, data processing, reproducibility, and interpretation across the function.
Apply quantitative, statistical, machine-learning, and agentic approaches to improve experimental design, data analysis, troubleshooting, and scientific decision-making.
Partner with protein production, protein engineering, cell biology, pharmacology, prototyping lab, computational drug design, and therapeutic-application teams.
Directly manage, mentor, and develop junior scientists and research associates, including providing technical direction, setting priorities, and supporting career development.
Communicate complex technical findings and program recommendations to project teams, senior leadership, external collaborators, and technology partners.