Member of Technical Staff, Vibe Labs

Generate Biomedicines•Somerville, MA

About The Position

About Generate:Biomedicines Generate:Biomedicines is a new kind of therapeutics company – existing at the intersection of machine learning, biological engineering, and medicine – pioneering Generative Biology™ to create breakthrough medicines where novel therapeutics are computationally generated, instead of being discovered. The Company has built a machine learning-powered biomedicines platform with the potential to generate new drugs across a wide range of biologic modalities. This platform represents a potentially fundamental shift in what is possible in the field of biotherapeutic development. We pursue this audacious vision because we believe in the unique and revolutionary power of generative biology to radically transform the lives of billions, with an outsized opportunity for patients in need. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us! Generate:Biomedicines was founded in 2018 by Flagship Pioneering and has received nearly $700 million in funding, providing the resources to rapidly scale the organization. The Company has offices in Somerville and Andover, Massachusetts with 300+ employees. The Role: As a member of our technical staff, you will take on a hands-on AI research role focused on building, deploying, and scaling autonomous scientific systems in production environments. You will design, implement, and operationalize agentic AI systems that generate molecular designs, propose and refine hypotheses, interact with experimental data, and improve through structured feedback. A central part of this role is defining the evaluation systems, operational workflows, and technical infrastructure required to scale AI-driven scientific discovery in a measurable and responsible way, integrating scientists, experiments, and AI into continuous feedback loops. If you are motivated by building AI systems that reason about science, generate testable ideas, and learn from real experimental outcomes, this role offers the opportunity to push the frontier of AI-enabled science.

Requirements

Strong AI Research Foundations:
Deep understanding of modern ML systems, with experience designing and evaluating complex models or AI-driven workflows. Publications are valued but not required; evidence of technical depth through open-source projects or shipped systems is equally meaningful.
AI x Science Fluency:
Bachelors or advanced degree in a scientific discipline (Biology, Chemistry, Physics, or related fields) and 2+ years of relevant experience — all are welcome; we care about capability, not tenure. Familiarity with molecular design workflows (biologics, small molecules, or materials) is a strong plus, but exceptional ML engineers motivated to ramp into biology are encouraged.
Agentic Systems Experience:
Hands-on experience building agent systems. You understand planning, tool integration, memory, evaluation, and iterative refinement.
Evaluation-Oriented Thinking:
Experience designing benchmarks, metrics, or experimental protocols for ML systems — particularly in settings involving iterative feedback and human oversight.
Strong Engineering Capability:
Demonstrated ability to design, build, and deploy production-grade ML systems. Proficiency in Python and experience working across modeling and cloud infrastructure. Comfort operating in modern cloud environments.
Interest in Scalable Scientific Automation:
Curiosity about distributed compute, infrastructure for large-scale experimentation, and emerging paradigms in AI-driven discovery.

Responsibilities

Design and Build Autonomous Discovery Systems
Design and implement autonomous AI systems that integrate molecular design and modeling (protein design, small molecules, materials) with structured experimental feedback loops.
Operationalize Scientist–AI–Experiment Workflows:
Architect workflows in which agents, scientists, and experimental platforms iteratively refine designs and hypotheses, rather than operating in isolation.
Develop Evaluation Frameworks for AI Scientists:
Create rigorous benchmarks and evaluation protocols to measure agent performance in scientific reasoning, hypothesis generation, molecular proposal quality, and closed-loop iteration. Define metrics that enable meaningful acceleration of discovery.
Prototype, Deploy, and Iterate:
Build research prototypes to test new agent capabilities, then formalize successful approaches into robust internal systems.
Translate Scientific Questions into Agent Workflows:
Convert high-level biological or therapeutic questions into structured, executable AI workflows with measurable outcomes.
Contribute to the Scientific Community:
Share findings through technical writing, open-source releases, or selective publications where appropriate.