Scientist, Large Libraries

About the role

THIS IS CRADLE

Proteins are the molecular machines of life, used for many therapeutic, diagnostic, chemical, agricultural and food applications. Designing and optimizing proteins takes a lot of expert knowledge and manual effort, through the use of custom computational and biological tools.

Machine learning is revolutionizing this space, by enabling high-fidelity protein models. At Cradle, we offer a software platform for AI-guided discovery and optimization of proteins, so that biologists can design proteins faster and at scale. We are already used by clients across biopharma, biotech, agri-tech, food-tech, and academia.

We're an experienced team of just under 100 people. We've built many successful products before and have enough funding for multiple years of runway. We are distributed across three offices in Amsterdam, Boston, and Zurich, alongside a few remote colleagues.

We offer our employees a very competitive salary, a generous equity stake in the company and a wide range of benefits and career progression opportunities.

WHAT WE'RE LOOKING FOR

We are looking for a (Senior) Scientist to join our Large Libraries team and drive the development of novel high-throughput screening capabilities for protein-protein interactions. In this role, you will work on developing novel methods to screen protein variant libraries (e.g. antibodies) for measurement of protein interaction binding affinities. You will develop innovative methods to explore sequence-function landscapes, generating large-scale datasets. These datasets will power the Cradle AI/ML platform models, strengthen design recommendations, accelerate protein optimization, and deepen understanding of protein performance. Your efforts will enable our customers to engineer better proteins, faster across biotechnology applications.

RESPONSIBILITIES

Method Development & Execution

- Develop and optimize novel workflows for high-throughput protein-protein interaction library screening

- Build and optimize in vitro or cell-based assay pipelines across multiple display and screening modalities (yeast, phage, mRNA, ribosome display, or mammalian display)

- Design, build, and execute molecular biology and biochemistry frameworks for library construction, screening, and data generation at scale

- Establish robust and repeatable assays with the statistical rigor required for ML model training, evaluation and optimization

Technical Leadership

- Drive and lead projects from experimental design through data delivery

- Identify opportunities to improve throughput, data quality, repeatability, and workflow efficiency.

- Collaborate with a cross-functional team to troubleshoot complex technical challenges and iterate on solutions.

Cross-functional Collaboration

- Partner with the ML team to define data requirements and integrate experimental outputs into the ML pipeline.