Software Engineer in Machine Learning

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.

Machine learning for biology has advanced rapidly over the past years and produced astonishing results. Most famous of all is probably AlphaFold, a protein structure prediction model based on a transformer architecture. Researchers have been able to transfer the amazing progress in Natural Language Processing to the domain of proteins, creating models that effectively "speak" the language of proteins. While interacting with biologists we recognized they would love to leverage these models, but lack the expertise to adapt them to their needs. This is where Cradle jumps in: we focus on taking state of the art intelligent algorithms and adapt them to solve concrete protein engineering problems, such as increasing thermal stability, solubility or activity for various enzymes. It is fascinating to see how machine learning and biology interact

For this challenge we are looking for software engineers who are excited to peek out of the virtual world and join us on integrating promising ML models in a protein design platform. 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. We look for candidates who are not shy to take research papers or ML prototypes and turn them into scalable product features. Besides dealing with the ML models itself, a big part of your job will be to build the necessary infrastructure around the machine learning models to make them accessible and useful to biologists.

Responsibilities:

As a software engineer in machine learning, you would be responsible to:

• Take an algorithm from a research project and transform it into robust, well-tested, functional code

• Support the team in establishing a stable, high quality and flexible software engineering process.

• Set up validations and benchmarks to ensure a high quality of ML models.

• Collaborate with biologists, software engineers and scientists alike and learn to understand their domain

• Contribute to open source software and establish a community around Cradle and its platform

• You have 3+ years of experience in developing machine learning systems

• You are at home in PyTorch or JAX

• You have strong problem solving skills, and an exquisite mastery of fundamental algorithms and data structures. You write readable code.

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

• You have experience with developing backend systems in modern cloud environments

• You have worked with natural language processing models or models applied to protein sequence-to-function relationships

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 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.