Scientist I, Synthetic Biology

Generate Biomedicines•Somerville, MA

About The Position

Generate Biomedicines’ Prototyping Lab develops experimental systems to measure protein function at scale by combining synthetic biology, large DNA libraries, high-throughput assays, and machine learning. The company is hiring two Scientists to develop pooled screens that test hundreds of thousands of protein variants in parallel to identify protein binders and modulators. The work includes DNA library design, pooled in vitro protein display, and NGS-based enrichment analysis. One of the two roles additionally calls for experience in mammalian cell engineering and membrane protein studies. Computational modeling or statistical analysis experience can help connect experimental design to data quality and usefulness. The Scientist will collaborate closely with Protein Sciences and Machine Learning teams to build screening approaches that provide clear, quantitative insight into protein function. The role offers scope to originate and advance technologies in collaboration with a well-resourced biotech team. Responsibilities span developing screening workflows and reliable assay conditions, interpreting data, evaluating new hypotheses, informing drug programs and IP filings, and communicating recommendations across disciplines. Generate Biomedicines is a clinical-stage generative biology company founded in 2018. It integrates machine learning and large-scale experimentation to design proteins with defined biological intent and advance protein-based medicines. The company is committed to equal employment opportunity.

Requirements

PhD in synthetic biology, systems biology, bioengineering, or a related field, with 0–1 year of experience; alternatively, a bachelor’s or master’s degree with at least 6 years of highly relevant experience.
Hands-on experience designing and running pooled binding screens, ideally with both purified proteins and cell-based assays.
Experience designing and building pooled DNA libraries and using next-generation sequencing to track enrichment in selection campaigns.
Ability to interpret complex screening data and write scripts in Python or a similar language.
Ability to apply quantitative reasoning and experimental judgment to assess data quality, troubleshoot assays, and guide next steps.
Ability to take ownership of applied biology projects from concept through execution, analysis, and delivery.
High level of cross-functional autonomy and self-motivation.
Intellectual curiosity, willingness to share expertise, and ability to contribute to colleagues’ growth through collaboration and mentorship.

Nice To Haves

For one of the two roles: strong cell-culture skills and experience engineering cell lines for screening applications using methods such as CRISPR, lentiviral transduction, or transposon-based integration.
For one of the two roles: experience working with membrane proteins.
Experience in computational modeling or statistical analysis.
Familiarity with recombinant protein production, purification, and/or binding assays.
Familiarity with AI-assisted coding tools such as Cursor or other LLM-based platforms.

Responsibilities

Build pooled screening workflows for scalable protein-binding and functional readouts, from assay setup through data interpretation.
Collaboratively establish reliable assay conditions, quantitative measures, and handoff plans so project teams can maximize platform utility.
Identify opportunities to test unconventional hypotheses and apply promising concepts to accelerate drug programs and support IP filings.
Synthesize findings into clear recommendations and present them orally and in writing to colleagues across disciplines.
Use quantitative reasoning, modeling, and critical literature review to inform experimental choices.
Take ownership of applied biology projects from initial concept through execution, analysis, and delivery.
Troubleshoot assays, assess data quality, and guide next steps using sound experimental judgment.
Collaborate with Protein Sciences and Machine Learning teams to develop screening approaches that provide quantitative insight into protein function.