Scientist I / Scientist II, Computational Protein Generation

The Role:  We are  seeking  a creative, motivated Computational Scientist to join our Model-Driven Design team at  Generate:Biomedicines . You will join a talented and collaborative group of ML scientists, engineers, and wet-lab scientists dedicated to redefining how medicines are made. This role sits at the intersection of machine learning, structural biology, and therapeutic development, where you will stay at the leading edge of internal and external models for de novo protein design and systematically benchmark, integrate, and apply them within tightly integrated design–build–test–learn cycles to advance our therapeutic pipeline and impact patients' lives.   The ideal candidate combines deep structural intuition with a demonstrated ability to rapidly assess and apply protein design methods and metrics across diverse design problems, thinks in terms of reusable capabilities, benchmarks, and feedback loops across applications, and uses modern generative models and experimental readouts to guide iterative design cycles across modalities. You  don't  just run existing tools; you understand  what's  missing from current approaches and are driven to fill those gaps.   This role is based in our Somerville, MA office with flexibility for hybrid work.   Here's how you will contribute: Model application and optimization : develop,  validate , and productionize de novo protein generation protocols and optimization techniques on our experimental platform, using measured data in-the-loop to iteratively refine models across modalities and therapeutic applications.   Define and implement in silico metrics : design, interpret, and implement biophysical and functional metrics for evaluating generated designs,  leveraging  existing literature, adapting known metrics to new contexts, and performing original research to benchmark and deploy new scoring approaches.   Benchmark foundation models and guide their application : rigorously evaluate new models and tools and provide quantitative conclusions on where they are best applied to generate new therapeutics, including designing systematic internal benchmarks and discovering how to expand model capabilities to prosecute new therapeutic targets in novel ways and to maximize reuse across targets and programs.   Propose new therapeutic strategies :  identify  and implement solutions to create new therapeutics through mechanisms of action unlocked by de novo tools and modalities.   Partner cross-functionally to drive therapeutic development : work closely with experimental colleagues, biologists, and clinical scientists to define design  objectives , interpret experimental readouts, and guide iterative design-build-test-learn cycles that advance programs.   Advance the  state of the art : push forward sequence–structure–function understanding with a focus on reusable platform capabilities and model-informed feedback loops.   Integrate agentic tools into workflows:   leverage  agentic AI tools to rapidly  iterate on  models, benchmarks, scores, critics, and other analysis tools, accelerating the pace of discovery.   Build production-quality tools : develop robust, production-ready code in a collaborative team setting and present scientific progress in regular research meetings.   What Success Looks Like   First 3 months:  You have familiarized yourself with the proprietary Generate platform and either completed a first-pass design for a therapeutic program or built and applied your first internal tool or benchmark.   By 6  months:  You are fully fluent in the Generate stack. You have taken ownership of building or extending a segment of the platform applied to protein design, including designing or  maintaining  key benchmarks or metrics, or you are contributing materially to an active therapeutic program, driving design decisions with increasing independence.   By 12 months:  Given strategic direction, you  operate  with full autonomy scoping, building, and deploying new tools and methods that advance our protein design capabilities and therapeutic pipeline and strengthen our continuously learning, model-informed design platform.   The Ideal Candidate will have: PhD in Computational Biology, Biophysics, Computer Science, or a related field, with  demonstrated  experience in protein design applications.   0–2 years of experience applying computational and/or ML methods to protein design, modeling, or prediction.   Hands-on experience with machine learning and generative modeling for protein design, including familiarity with modern methods such as  RFDiffusion ,  ProteinMPNN ,  BindCraft ,  BoltzDesign , or equivalent approaches and how to deploy or evaluate them in practice.   Strong structural intuition and understanding of protein biophysics with the ability to quickly assess and adapt design methods and metrics to  new problems .   Familiarity with protein