Scientist, Large Libraries

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.

• Communicate results, insights, and technical challenges effectively across disciplines.

• Contribute to shaping the scientific direction of the Large Libraries team and meaningfully contribute to the advancement of the Cradle platform.

Your background

Missing one or two points from the list below? No worries, if you're excited about this role and meet most of these criteria, we definitely want to hear from you. In the application form you will have a chance to spark our curiosity by telling us something groundbreaking you have worked on.

• PhD in biochemistry, molecular biology, biophysics, synthetic biology, bioengineering, or a related field.

• Demonstrated creativity in method development: you have invented or substantially adapted workflows, not just executed established ones.

• Excitement to learn, contribute, and drive innovation in an early stage startup environment. Be comfortable with ambiguity and a fast paced environment.

• Strong verbal and written communication skills in English. Proactively sharing results, successes and challenges in a cross-functional environment.

• Ability to run multiple projects simultaneously while ensuring that process steps are documented, and physical/digital data are organized.

Nice-to-haves

Experience with one or more of the following would be an advantage:

• Large-scale DNA library construction methodologies (>10^6 variants)

• Display platforms (yeast, phage, mRNA, ribosome display, or mammalian display)

• Flow cytometry and FACS

• Familiarity with binding kinetic measurement techniques (SPR, BLI, ELISA, etc.)

• Next-generation sequencing library preparation, QC, and bioinformatic analysis tools (Illumina, Nanopore, PacBio).

• Experience with statistically rigorous experimental design and data quality assessment

• High-throughput data analysis or familiarity with scripting languages (Python, R)

• Classical high-throughput laboratory automation (robotic liquid handlers, plate readers, colony pickers)

Learning more about the BioEngineering team

We're quite open about what we work on in our BioEngineering team. If you'd like to learn a bit more before applying, check out blog posts from our team (link 1, link 2) or watch our webinar on lab automation.

Our commitment to inclusive hiring

Cradle evaluates all candidates based on merit, regardless of sex, gender, ethnicity, socio-economic background, or any other aspect of identity. We maintain zero tolerance for discrimination and actively encourage candidates from all backgrounds to apply.

AI disclosure

Cradle uses an applicant tracking system that includes basic AI-assisted features which may generate scores or rankings based on how applications match a job description. These outputs are not used to make or meaningfully influence hiring decisions — all candidates are evaluated through a thorough, human-led review by our recruiting team.

A notice about recruitment scams

Please be aware that scammers are posing as us in order to get your personal details or money. We only communicate via @cradle.bio email addresses, we only make job offers after having met you in person at our office in Zurich or Amsterdam, and we never ask you to pay for anything during the interview process.

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