AI Engineer

Benefits

Additional Information

Join us to shape EMBL AI , a new EMBL-wide initiative that aims to exploit the full potential of AI-based approaches to advance scientific discovery. The European Molecular Biology Laboratory (EMBL) has long been a pioneer in developing and applying Artificial Intelligence (AI) to advance research in genomics, structural biology, and drug discovery. Innovative AI models like AlphaFold have already revolutionised protein structure prediction, and EMBL AI is now building the infrastructure, expertise, and community needed to take this further - across all of EMBL's missions and the wider life sciences research community. As part of this initiative, EMBL AI is establishing an AI Engineering & Automation Team at its Heidelberg Hub. As AI Engineer, you will be a founding member of this team - providing shared engineering capacity across EMBL-AI, contributing to a portfolio of pilot and flagship projects, and acting as a critical bridge between AI research and scientific practice. Your work will be underpinned by EMBL's strong computing environment, including on-site GPU infrastructure, modern research IT platforms, and access to European AI compute ecosystems, and a key part of your role will be to translate this capacity into reliable, usable AI services for the scientific community. Your role This is a shared resource role with a strong service orientation. You will provide engineering capacity to multiple research groups and teams across EMBL, ensuring that AI methods and infrastructure are accessible, reliable, and fit for scientific purpose. A key aspect of the role is to act as a "lab in the loop" bridging engineer - someone who can work directly alongside experimental and computational scientists to understand their needs, prototype AI-driven solutions, and iteratively develop tools that integrate seamlessly into research workflows. Your responsibilities include (but are not limited to) the following: Provide shared AI engineering capacity to EMBL research groups, acting as a practical bridge between cutting-edge AI methods and their real-world application in laboratory and computational settings Design, develop, and deploy ML/DL models, pipelines, and infrastructure (including model training, evaluation, versioning, and deployment), ensuring tools are robust, reproducible, and accessible to the scientific community Contribute to shared AI platforms, APIs, and software libraries, applying state-of-the-art approaches including large language models, computer vision, and multimodal methods to biological research questions Contribute to open science by publishing code, models, and benchmarks, and support knowledge transfer across teams and sites through clear documentation The role is based at EMBL Heidelberg but will involve interactions with other EMBL sites and some international travel. You have An advanced university degree in computer science, machine learning, mathematics, computational biology, or a related discipline Ability to design, own, and evolve complete AI-enabled systems (from infrastructure and orchestration through model serving, APIs, and user-facing tools) with strong architectural judgement about automation, human-in-the-loop decisions, and safe AI integration Strong systems engineering background, including shell environments, distributed systems, networking, and the ability to debug complex, multi-component deployments Experience with modern infrastructure for AI systems, including Docker, Kubernetes, CI/CD pipelines, GPU environments, model serving frameworks, observability tooling, and scalable deployment Experience with large language models, agentic systems, and AI-assisted software development workflows Ability to decompose ambiguous scientific needs into robust, well-scoped technical components and make sound infrastructure and design choices under uncertainty Motivation to build systems that interact with real scientific environments, including laboratories, instruments, data platforms, and research workflows A genuine service orientation, i.e. the ability and motivation to work responsively across multiple projects and teams simultaneously, ensuring resulting tools are reliable, usable, maintainable, and scientifically meaningful Strong communication skills and the ability to engage effectively with both technical and non-technical scientific stakeholders Fluency in spoken and written English You might also have Experience applying AI to biological or biomedical data (e.g. genomics, protein structure, bioimage analysis, single-cell data) Familiarity with MLOps practices and tools (e.g. MLflow, Weights & Biases, Kubeflow) Experience with large language models, multimodal models, or foundation models in a research or applied setting Experience with laboratory automation or AI-driven experimental design Knowledge of data management and FAIR data principles in a research context Experience contributing to open-source software projects Prior work in an academi

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at embl? Share your experience