Member of Technical Staff, Vibe Labs

Generate Vibe Labs is a frontier lab within Generate:

Biomedicines, designed for focused, entrepreneurial teams pushing the edges of generative biology, AI, and therapeutic engineering to create new capabilities in sync with the Generate Platform. Vibe Labs operates like a startup at the edge of the platform, small, experimental, and fast-moving, designed for builders who thrive in ambiguity and want to turn bold scientific ideas into high-impact medicines and technologies.

The Role:

As a Member of Technical Staff, you will work hands-on to build AI systems that support scientific discovery. You will contribute to the development of autonomous and semi-autonomous AI tools that generate molecular designs, propose hypotheses, interact with experimental data, and improve through experimental feedback.

You'll collaborate closely with fellow scientists, engineers, and experimental teams to prototype and iterate on agent-based systems, helping translate scientific problems into AI discovery pipelines.

If you're excited about applying AI to real scientific questions - and orchestrating models to scale up the ideation, design, and verification cycle in meaningful ways - this role offers the opportunity to grow at the frontier of AI x science.

Here's how you will contribute:

Build and Improve Science Discovery Systems

Contribute to the design and implementation of scalable, autonomous, and results-driven AI systems that integrate molecular design with experimental feedback loops. Be familiar with modern AI-assisted development practices. Build and test research prototypes to explore new agent capabilities and orchestration patterns.

Support Experimental Verification Workflows

Work with cross-functional teams to develop workflows where AI systems assist scientists in refining and verifying designs and hypotheses, enabling more efficient iteration. Translate high-level scientific goals into executable software pipelines that autonomously navigate the design space.

Contribute to Knowledge Sharing

Document findings, share learnings internally, and contribute to technical reports, open-source efforts, or publications when appropriate.

The Ideal Candidate will have:

A Builder Mindset

You are comfortable deploying full-stack AI services and engaging in rapid, iterative exploration. Leverage next-generation AI coding tools to accelerate ideation and validation velocity. Adaptability to new concepts and frameworks. You can quickly adapt to new concepts, tools, and frameworks, and apply them to solve problems.

Strong ML Foundations

Solid understanding of modern machine learning methods (e.g., deep learning, generative models, foundation models). Experience training, fine-tuning, or evaluating ML systems through research projects, internships, open-source work, or industry experience. Publications are valued but not required.

AI × Science Fluency

Bachelor's or advanced degree in a scientific or quantitative field (e.g., Biology, Chemistry, Physics, Computer Science, Engineering, or related). Exposure to molecular modeling, protein design, small molecules, or computational biology is a plus - but strong ML candidates motivated to learn biology are encouraged to apply.

Experiences in Building Agent Systems

Hands-on experience building agent systems and familiarity with common design patterns. Familiarity with agent evaluation frameworks. Experience working with cloud platforms (GCP, AWS, Azure), and/or alternative serverless databases and distributed computing frameworks.

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…