Machine Learning Researcher | Protein Design | Diffusion Modelling | Flow Matching | Zurich, Switzerland

h3About the role /h3 /brpAn innovative, high-growth AI-driven biotechnology company is looking for an experienced Machine Learning Researcher specialising in bprotein structure modelling /b to help develop advanced machine learning systems for protein discovery, engineering and optimisation. /p /brpThe company is building a machine learning platform that combines computational modelling with experimental validation, supporting applications across industries including biopharma, agriculture, food and industrial biotechnology. /p /brpIn this role, you will contribute to the structural modelling capabilities underpinning both protein engineering and de novo protein design. Key research areas include: /p /brul /brlibStructure and complex prediction: /b Training and fine-tuning protein structure prediction and co-folding models. /li /brlibStructure-conditioned property prediction: /b Developing models that use predicted and experimental structures to predict multiple protein properties. /li /brlibStructure representation learning: /b Working with equivariant architectures and structural encoders. /li /brlibActive learning: /b Designing and implementing active learning strategies to optimise the design-build-test-learn cycle and maximise the value of experimental data. /li /br /ul /brpYou will be expected to push the boundaries of machine learning research in protein engineering and de novo design. Working closely with research scientists and engineers, you will take novel approaches from initial research through to robust, production-ready implementations, with a strong emphasis on scalability and software engineering standards. /p /brh3Research environment /h3 /brpA key advantage is access to a dedicated in-house wet lab, enabling rapid experimental validation of protein designs and research hypotheses. Short experimental turnaround times and high-throughput capabilities allow researchers to test ideas quickly and generate valuable proprietary datasets. /p /brpThis creates a direct feedback loop between computational research and experimental biology, allowing you to assess the real-world performance of your models and continuously improve them. /p /brh3Requirements /h3 /brh3Essential technical experience /h3 /brul /brliPhD in Computer Science, Mathematics, Physics, Structural Biology, Computational Biology or a related discipline. /li /brliHands‑on experience building and training protein structure models, rather than simply running existing tools. Relevant experience includes structure or complex prediction, co‑folding, backbone generation, inverse folding and structure‑conditioned property prediction. /li /brliStrong knowledge of geometric deep learning for molecules, including equivariant architectures, frame‑or‑coordinate‑based representations, and attention mechanisms over 3D structures. /li /brliExperience with modern generative modelling techniques, such as diffusion models, flow matching and masked language modelling. /li /brliStrong statistical and applied mathematical skills, particularly in evaluating results from small, noisy or confounded datasets. /li /brliFamiliarity with PyTorch or JAX. /li /br /ul /brh3Essential personal attributes /h3 /brul /brlibSelf‑directed: /b Comfortable tackling abstract research problems independently, structuring your work and delivering results without constant supervision. /li /brlibGrowth‑oriented: /b Motivated by challenging technical problems, continuous development and working in a fast‑moving environment. /li /brlibStrong communicator: /b Able to communicate effectively across disciplines, particularly at the intersection of biology and software engineering. /li /brlibCollaborative: /b A kind, supportive team player who contributes to a positive and constructive working environment. /li /brliLarge‑scale model training, including multi‑node and sharded training, and managing long‑running training workloads. /li /brliML infrastructure, model provisioning, orchestration and deployment tools, such as Kubernetes and MLflow. /li /brliProgramming languages beyond Python, particularly Rust, Haskell, Julia or Elixir, and experience with functional programming paradigms. /li /brliInference optimisation, including Triton, Pallas and custom attention kernels for structural data. /li /brliExperience taking deep learning models from research into production. /li /brliExperimental structural biology, including crystallography, cryo‑electron microscopy (cryo‑EM) or NMR, and understanding how experimental artefacts affect training data. /li /brliPhysics‑based modelling, including molecular dynamics, docking and force fields, with an understanding of their strengths and limitations relative to learned models. /li /brliFamiliarity with protein design and synthetic biology applications, such as bispecifics, CAR‑T and metabolic engineering. /li /br /ul /brh3The opportunity /h3 /brpThis is an opportunity to work at the intersection of bmachine learning research, computational protein design and experimental biology /b, developing novel models with a direct route to experimental validation. /p /brpYou will have the opportunity to shape research direction, work on technically challenging problems and help translate cutting‑edge machine learning methods into practical protein engineering applications. /p #J-18808-Ljbffr