Senior Scientist I, Computational Protein Design

The Role:

Generate is seeking a creative and rigorous scientist to develop computational technologies and lead applied platform research that expand what protein therapeutics can do. You will develop reusable methods, scoring and evaluation tools, model benchmarks, agentic systems, and scientific workflows for protein design, engineering, and optimization. Your work will span creating new proteins, modifying existing molecules, and integrating molecular components to unlock new therapeutic applications.

At Generate, model development, protein design, high-throughput experimentation, structural characterization, and therapeutic development operate within one organization. This role offers the opportunity not only to advance computational methods, but to see them shape what proteins are built and tested, learn from rich in-house experimental data, and contribute to medicines in our pipeline. We are looking for someone who is motivated by both technical innovation and its application to drug development.

This role combines technology development with applied platform research. You will identify recurring scientific needs, lead research efforts, and turn successful approaches into durable capabilities with clear interfaces and validated performance. Working with program teams, biologists, and experimental scientists, you will test and refine these capabilities in relevant biological settings, establish proof of concept for new molecular properties and functions, and combine validated approaches to enable new therapeutic applications and pipeline programs. You will lead the computational research needed to establish feasibility, resolve technical risks, and make successful approaches reusable across targets and programs.

Here's how you will contribute:

• Lead technology development and applied platform research. Identify high-value capability gaps and molecular or biological challenges, formulate testable hypotheses, and define research plans, milestones, and decision criteria. Drive projects from scientific question through computational implementation and experimental proof of concept to validated, reusable capabilities.
• Build and evaluate reusable protein engineering capabilities. Develop computational methods for protein design, optimization, and interaction modeling using generative, predictive, and biophysical approaches. Create scores and benchmarks to assess sequences, structures, biological functions, and developability. Define intended uses, interfaces, assumptions, and success criteria across targets, molecular formats, and therapeutic applications. Build reproducible datasets and evaluation protocols that reveal strengths and failure modes, quantify uncertainty, and guide appropriate use.
• Engineer and validate new molecular capabilities. Partner with assay, structural, and experimental scientists to design informative molecular libraries and iterative design–build–test–learn studies. Demonstrate control of properties such as binding specificity, conditional activity, molecular assembly, stability, or developability. Analyze experimental readouts, distinguish signal from noise, diagnose failures, and use the results to improve methods and establish where they generalize.
• Scale scientific capabilities. Partner with machine learning and engineering to translate platform and application needs into model objectives, evaluation criteria, and data requirements. Stress-test new models and work with engineers to turn successful research into reliable, maintainable software.
• Build agentic systems for therapeutic protein design. Develop agentic systems and multi-agent workflows that connect scientific information, generative and predictive models, internal data, analysis tools, and experimental results to formulate hypotheses, propose designs, evaluate evidence, and recommend informative next steps. Establish mechanisms for enabling human oversight, traceability, and rigorous measures of reliability.
• Enable new therapeutic applications. Work with application teams to combine validated capabilities into approaches that address consequential molecular or biological challenges. Establish technical feasibility in therapeutically relevant settings, assess performance against appropriate references, and test whether an approach can support multiple targets or programs. Generate evidence that informs advancement or discontinuation, target selection, and nomination of differentiated therapeutic programs.
• Transfer and disseminate successful approaches. Turn research outcomes into documented methods and reference workflows that other teams can use and interpret reliably. Support adoption by application and program teams, and translate lessons across projects into improvements that benefit the broader platform.

The Ideal Candidate will have:

• Strong scientific and quantitative foundations, with practical fluency in modern machine learning and computational methods relevant to protein science, molecular modeling, or biological design.

…