Scientist I, Synthetic Biology

The Role:

Generate’s Prototyping Lab builds the experimental systems that let us measure protein function at unprecedented scale. By combining synthetic biology, large DNA libraries, high-throughput assays, and machine learning, the team creates workflows that turn complex biological questions into rich, usable datasets.

We’re looking for two Scientists with a strong record in experimental synthetic biology to develop pooled screens capable of testing hundreds of thousands of protein variants in parallel to identify protein binders and modulators. You’ll shape these experiments via DNA library design, pooled in vitro protein display, and NGS-based enrichment analysis. For one of the two roles, we are looking for a Scientist who additionally has experience in mammalian cell engineering and membrane protein studies. Experience in computational modeling or statistical analysis would help you connect experimental design to the quality and usefulness of the resulting data.

You’ll work closely with Protein Sciences and Machine Learning teams to build screening approaches that deliver clear, quantitative insight into protein function. The role offers substantial scope to originate and advance new technologies, with close collaboration across a well-resourced biotech team.

Here's how you will contribute:

• Build pooled screening workflows to achieve scalable protein binding and functional readouts, from assay setup through data interpretation.
• Collaboratively establish reliable assay conditions, quantitative measures, and handoff plans that enable project teams to maximize platform utility.
• Identify opportunities to test unconventional hypotheses, applying the strongest concepts for drug program acceleration and supporting IP filings.
• Synthesize findings into clear recommendations, and present them in presentations and in writing to colleagues across disciplines.
• Use quantitative reasoning, modeling, and critical literature review to inform experimental choices.

The Ideal Candidate will have:

• PhD in synthetic or 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 using both purified proteins and cell-based assays.
• For one of the roles, we are seeking strong cell-culture skills, including experience engineering cell lines for screening applications using methods such as CRISPR, lentiviral transduction, or transposon-based integration; experience working with membrane proteins is also highly preferred.
• Experience designing and building pooled DNA libraries, and using next-generation sequencing to track enrichment in selection campaigns.
• Familiarity with recombinant protein production, purification, and/or binding assays is an advantage.
• Ability to interpret complex screening data and write scripts in Python or a similar language; familiarity with AI-assisted coding tools such as Cursor or other LLM-based platforms.
• Applies quantitative reasoning and sound experimental judgment to assess data quality, troubleshoot assays, and guide next steps.
• Takes ownership of applied biology projects from initial concept through execution, analysis, and delivery.
• Brings intellectual curiosity to new technical challenges, shares expertise readily, and helps colleagues grow through collaboration and mentorship.
• High level of cross-functional autonomy and self-motivation.

Who will love this job:

• Biologists who integrate computational insight with experimentation to tackle complex problems.
• Experimentalists who enjoy making difficult targets, including membrane proteins, tractable for pooled binding screens.
• Platform builders who value assay robustness, data quality, and interpretable results alongside throughput.

#LI-HM1

About Generate Biomedicines

We are a clinical-stage generative biology company pioneering the AI revolution in drug design and development. We are advancing a new approach to drug creation—one grounded in the ability to design proteins with defined biological intent. By integrating machine learning with large-scale experimentation, this approach aims to reduce the uncertainty, time, and cost associated with developing protein-based medicines.

Founded in 2018, we are advancing a growing pipeline of clinical and preclinical programs across multiple disease areas and protein modalities. By unifying computational design and clinical development within a single operating model, we translate this approach into clinical-stage programs and are leading a shift from traditional drug discovery toward systematic drug generation.

At Generate:Biomedicines, we collaborate across disciplines in new ways to invent and innovate. We bring diverse perspectives to a shared goal of delivering better medicines to patients in need, faster, guided by our values and leadership behaviors.

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