Machine Learning Research Engineer

Our Computational Sciences group is a team of machine-learning engineers, computational chemists, and drug-discovery scientists. We build in-silico tools that let our Medicinal-Chemistry and Biology colleagues make faster, smarter decisions throughout hit-finding, hit-to-lead, and lead-optimization.

We're searching for research engineers who love creating algorithms, models, and systems that unlock value in small-molecule drug-discovery workflows and advance the state-of-the-art in ML for chemistry.

The role could be either based in Switzerland, the UK or the US.

Key qualifications

• Hands-on deep-learning expertise with a focus on molecular-representation learning: graph neural networks (GNNs), 3-D transformers, diffusion/flow models, or large language models for chemistry (e.g., SELFIES/SMILES transformers).
• Solid technical foundations in mathematics and statistics;
• Proficient programming skills in Python and one or more of the ML frameworks such as PyTorch, Tensorflow, and JAX;
• Familiarity with MLOps & DevOps (Docker, Git, CI/CD, experiment tracking, cloud computer).
• Ability to work in a collaborative environment across different geographical regions;

What you'll do

We have seen that machine learning methods applied to problems in protein engineering can genuinely be impactful. Furthermore, these in-silico methods can be even more rewarding, when they are put to the test in our lab as part of our drug development programs. You will be working in a team of talented and driven engineers and scientists, who are striving to make a difference in drug development and open to applying novel techniques in order to contribute to our understanding of some of the most relevant areas of protein science.

The role mainly involves:

• Design novel ML/AI algorithms for small-molecule tasks e.g., property prediction, binding-affinity estimation, activity cliffs, ADME/Tox, and generative molecule design.
• Integrate physics-based methods with data-driven models to build hybrid, end-to-end pipelines.
• Build & own our chemistry-focused MLOps stack, taking models from research to robust production services.
• Explore blue-sky research directions while balancing near-term program needs.
• Collaborate closely with medicinal chemists and biologists, translating model insights into tangible design hypotheses and experimental campaigns.
• Communicate results to technical and non-technical stakeholders papers, internal seminars, open-source contributions, and conference talks.

Education & Experience

• PhD (or MS + 2 yrs experience) in Computer Science, Computational Chemistry, Cheminformatics, Chemical Engineering, Applied Physics, or a related field.
• Publications, patents, open-source repos, or shipped products demonstrating impact in AI-for-chemistry or computational drug discovery are highly valued.

We inform you that your personal data will be processed according to the applicable Data Protection Law. The Data Controller is Peptone Switzerland AG, which will process your personal data for employee recruitment purposes. To view the extended information notice, please select the 'apply to position' link here above.