Here's the tl;dr:

• We are on a mission to make programming biology easy. We provide biologists with the software, machine learning & services to discover, design and optimise enzymes and their cell-factories to ultimately produce almost anything.

• We're an experienced team. We have built many successful products before and we have raised enough funds to have plenty of runway for the years to come!

• We're focused on building the best possible team culture. We're distributed across two locations, and are flexible about when and where we work.

• We offer top of the market salary, a generous equity stake in the company and a wide range of benefits from health and wellbeing, financial, to training and career progression opportunities.

This is Cradle

Cradle helps teams to engineer proteins with fewer, more successful experiments. We use generative machine learning models, accessible through user-friendly software interfaces, to help engineer protein properties such as stability, expression, activity, binding affinity and specificity. We are bridge builders who bring together the best of machine learning, synthetic biology, developer tools, and user experience design. Our mission is to enable a world where most of the products around us can be easily made and degraded biologically using a cell-factory instead of (petro-)chemicals, plants or animal agriculture.

Position summary

For this challenge we are looking for machine learning experts with experience in using large language models for protein design, who are excited to apply their knowledge in an industrial setup. Be ready to witness first hand what happens when you leave the bits and bytes behind and try to solve challenges with nature’s constraints and complexity. Besides keeping up to speed with the cutting edge research in the field or ML applied in protein design, a significant part of your job will be to consult and help ML engineers turn ideas published in papers into code that works in production, by guiding the ML team in the most promising directions of research and sanity checking the models proposed by the ML team.

Responsibilities:

As a machine learning researcher, you will be responsible to:

• Develop an intuition for promising projects and propose ml-based approaches to bring on next

• Build industry relevant benchmarks and validations techniques for models trained on biological data

• Mentor junior Machine Learning engineers

• Collaborate closely with biologists, software engineers and scientists alike and learn to improve the design-build-test-learn loop

• Present scientific findings in journals, conferences, blog posts, etc.

Need to have (technical)

• PhD in Computer Science, Mathematics, Physics, Computational Biology or a related discipline.

• 3+ years of experience in applying deep learning to problems related to protein sequence-function relationships (e.g., protein property prediction, de novo design, structure prediction, directed evolution, etc.).

• Demonstrated ability to deliver deep learning models to production use.

Need to have (non-technical)

• You are productive by yourself. You will be asked to solve abstract tasks independently and produce results and organize your work without constant supervision.

• You want to grow. Cradle is a high-growth company providing a lot of new opportunities. You enjoy taking on challenging projects and are able to process honest feedback.

• You are able to communicate well. We are working at the intersection of biology and software where good communication is key.

• You are kind and work well in teams. We look for team players who contribute to a positive and friendly working environment.

Nice to haves

• Understand the biological problem a model is designed for and know where its weaknesses lie.

• We are setting up a highly automated ML model provisioning system. Experience with popular machine learning, pipelining and deployment libraries will be of high value.

• You have an understanding of genomics and molecular biology.

Cradle helps biologists design improved proteins in record time using powerful prediction algorithms and AI design suggestions.

We use generative machine learning models, accessible through user-friendly software interfaces, to help engineer protein properties such as stability, expression, activity, binding affinity and specificity. Our mission is to ultimately help more people build with biology and replacing traditional farms and factories for a more sustainable world.

You will be joining a high-growth, highly ambitious startup with an experienced founding team, whose motivation is building a more sustainable world using biology. We are backed by some of the most well known investors and industry veterans. Joining us now means you will be getting in on the ground floor; you will have a significant impact on how we shape the company, and success will mean an outsized reward.

Did we pique your interest? We'd love to hear from you. Please use this form to apply directly.