Scientist I Scientist II, Computational Protein Generation

The Role:

We areseekinga creative motivated Computational Scientist to join our Model-Driven Design team atGenerate: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 designbuildtestlearn 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. Youdontjust run existing tools; you understandwhatsmissing from current approaches and are driven to fill those gaps.

This role is based in our Somerville MA office with flexibility for hybrid work.

Heres how you will contribute:

• Model application and optimization: developvalidate 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 designsleveragingexisting 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:identifyand 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 designobjectives interpret experimental readouts and guide iterative design-build-test-learn cycles that advance programs.
• Advance thestate of the art: push forward sequencestructurefunction understanding with a focus on reusable platform capabilities and model-informed feedback loops.
• Integrate agentic tools into workflows:leverageagentic AI tools to rapidlyiterate onmodels 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 6months: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 ormaintainingkey 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 youoperatewith 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 withdemonstratedexperience in protein design applications.
• 02 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 asRFDiffusionProteinMPNNBindCraftBoltzDesign 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 tonew problems.
• Familiarity with protein therapeutic modalities such as antibodies mini-proteins VHHs peptides or enzymes and an eagerness to deepenexpertiseacross these within de novo design workflows.
• Proficiencyin Python and scientific computing; comfort working in a production codebase.
• Experience designing running or interpreting benchmarks for computational or generative methods and drawing quantitative conclusions about model applicability and limitations.

Strongly Preferred

• Experience designing executing and interpreting experiments and experimental data (e.g. binding assays stability measurements structural characterization) and using those readouts to inform computational design iterations.
• Familiarity with agentic AI tools and their integration into scientific workflows.
• Exposure tostructure-based design techniques and computational tools for modeling protein-protein interactions.
• Track recordof translating research ideas into working software or reusable platform components used across multiple projects or applications.

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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 creationone 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.

Generate:Biomedicines is committed to equal employment opportunity regardless of race color ancestry religion sex national origin sexual orientation age citizenship marital status disability gender identity or Veteran status.

Recruitment & Staffing Agencies: Generate:Biomedicines does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Generate:Biomedicines or its employees is strictly prohibited unless contacted directly by the Companys internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Generate:Biomedicines and the Company will not owe any referral or other fees with respect thereto.