Senior Machine Learning Engineer

Isomorphic Labs
Lausanne; LondonPosted 26 March 2026

Job Description

Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Senior Machine Learning Engineer Your impact Join our expanding Applied ML team and apply your expertise to cutting-edge ML models, specifically working with Bio models. This role offers the unique opportunity to collaborate closely with world-class researchers to drive innovation. Working in a highly creative, iterative environment, you will be partnering with leading engineers, scientists and ML researchers to build the critical platform driving that transformation. This is a newly created role and you will need to use your previous experience and show initiative in order to fully carve out your contribution. What you will do Partner with the ML Research, Drug Design, and Data Engineering teams to design, develop, train, and evaluate a variety of cutting edge ML models at unprecedented scale. Build a world-class ML drug design research environment with scalable software and libraries. Develop tools and libraries to enable large-scale machine learning experiments across thousands of accelerators. Maximise model performance, scalability, and robustness for production use within our computational platform. Create novel instrumental drug discovery tools in partnership with domain experts. Take end-to-end ownership from rapid prototyping to production-quality code. Iterate collaboratively with scientists and domain experts to deeply understand feature requirements and user feedback. Skills and qualifications Essential Strong experience with developing large-scale machine learning models. Extensive programming experience using any mainstream programming languages, including strong Python experience. Experience with modern ML frameworks including at least one of JAX, PyTorch or TensorFlow. Experience with the full ML research and development lifecycle. Experience partnering with research and product teams to prototype and ideally productionise ML models. Strong software engineering experience with software design / architecture skills. Strong understanding of ML theory and applications. Strong understanding of data structures and algori ... (truncated, view full listing at source)
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