Scientist I / Scientist II, Computational Protein Generation (AI)

TL;DR

Scientist I / Scientist II, Computational Protein Generation (AI/Biotech): Developing and optimizing de novo protein generation protocols using generative ML models with an accent on structural biology and model-driven design. Focus on building production-ready tools, implementing in silico metrics, and managing iterative design–build–test–learn cycles.

Location: Hybrid in Somerville, MA

Salary: $154,000 – $216,000 USD

Company

A clinical-stage generative biology company pioneering AI-driven drug design to create proteins with defined biological intent.

What you will do

• Develop, validate, and productionize de novo protein generation protocols and optimization techniques using experimental data in-the-loop.
• Design and implement biophysical and functional in silico metrics for evaluating generated designs.
• Rigorously benchmark foundation models to determine their optimal application for generating new therapeutics.
• Propose and implement new therapeutic strategies utilizing de novo tools and modalities.
• Collaborate cross-functionally with experimental biologists and clinical scientists to guide iterative design cycles.
• Build production-quality code and integrate agentic AI tools into discovery workflows.

Requirements

• PhD in Computational Biology, Biophysics, Computer Science, or a related field.
• 0–2 years of experience applying ML methods to protein design, modeling, or prediction.
• Hands-on experience with RFDiffusion, ProteinMPNN, BindCraft, BoltzDesign, or equivalent generative approaches.
• Strong structural intuition and understanding of protein biophysics.
• Proficiency in Python and scientific computing within a production codebase.
• Must be based in or able to work hybrid in Somerville, MA.

Nice to have

• Experience executing and interpreting experimental data (e.g., binding assays, stability measurements).
• Familiarity with integrating agentic AI tools into scientific workflows.
• Exposure to structure-based design and computational tools for protein-protein interactions.
• Track record of translating research ideas into reusable platform components.

Culture & Benefits

• Collaborative environment blending ML science, engineering, and wet-lab research.
• Hybrid work flexibility for roles based in the Somerville office.
• Compensation includes base salary, annual bonus, and equity.
• Competitive comprehensive benefits package.