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 revolutionising 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 pharma, biotech, agritech, foodtech, and academia.

We're an experienced team of roughly 60 people. We've built many successful products before and have enough funding for multiple years of runway. We are distributed across two main locations, Zurich and Amsterdam, and are focused on building the best possible team culture.

We offer our employees a very competitive salary, a generous equity stake (for full time employees) 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 high-throughput screening capabilities. You will bring deep expertise in microfluidics to complement our existing yeast display and FACS capabilities, enabling us to generate massive-scale datasets with >10^6 data points. These datasets will power the Cradle platform to strengthen design recommendations, accelerate protein optimization, and deepen our understanding of protein optimization across applications.

As a (Senior) Scientist, you will be a hands-on technical leader: designing and executing experiments, developing novel methods, and driving projects from concept to completion. You will work closely with our ML team to ensure experimental workflows generate the high-quality, ML-ready data our platform requires.

Responsibilities

Method Development & Execution

• Develop and optimize microfluidics-based workflows for high-throughput protein library screening and characterization

• Design and execute experiments for library construction, screening, and data generation at scale

• Establish assays with the statistical rigor required for ML applications

Technical Leadership

• Drive projects independently from experimental design through data delivery

• Identify opportunities to improve throughput, data quality, and workflow efficiency

• 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

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.

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

• Demonstrated hands-on experience with microfluidics for biological applications, such as:

  • Droplet microfluidics for high-throughput screening or directed evolution

  • Digital microfluidics platforms

  • Single-cell encapsulation and sorting

  • Emulsion-based assays for protein characterization

• Excitement to learn, contribute, and drive innovation in an early stage startup environment. Having an appetite for its ambiguity and fast pace.

• 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

• Next-generation sequencing library preparation and quality control (Illumina, Nanopore, PacBio)

• Experience with statistical experimental design or 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.

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.