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 therapeutic modalities such as antibodies, mini-proteins, VHHs, peptides, or enzymes, and an eagerness to deepen expertise across these within de novo design workflows.
Proficiency in 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 to structure-based design techniques and computational tools for modeling protein-protein interactions.
Track record of translating research ideas into working software or reusable platform components used across multiple projects or applications.

#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.

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 Company’s 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.

Compensation: The base salary range provided reflects our current estimate of what we anticipate paying for this position. Your actual base salary will be based on several factors, including job-related skills, experience, internal equity, relevant education or training, and market dynamics. In addition, you will be eligible for an annual bonus, equity compensation, and a competitive benefits package.

Per Year Salary Range

$154,000—$216,000 USD

Scientist I / Scientist II, Computational Protein Generation