EMBL
Molecular Biology
AIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer at EMBL. Skills: AI Engineering, ML/DL model development and deployment, AI infrastructure management, bridging AI research and scientific practice. 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”
What You'll Achieve.
advance scientific discovery; translate this capacity into reliable, usable AI services for the scientific community; ensuring AI methods and infrastructure are accessible, reliable, and fit for scientific purpose; prototype AI-driven solutions; iteratively develop tools that integrate seamlessly into research workflows; ensuring resulting tools are reliable, usable, maintainable, and scientifically meaningful; publishing code, models, and benchmarks; support knowledge transfer across teams and sites
Industry & Context.
Ability to decompose ambiguous scientific needs into robust, well-scoped technical components
some international travel
What They're Looking For.
Must Have
Ability to design, own, and evolve complete AI-enabled systems (from infrastructure and orchestration through model serving, APIs, and user-facing tools) with architectural judgement about automation, human-in-the-loop decisions, and safe AI integration, 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, Fluency in spoken and written English
Nice to 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 academic, research institute, or international organisation setting, Proficiency in other languages
What You'll Do.
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
and deploy ML/DL models
and infrastructure (including model training
ensuring tools are robust
and accessible to the scientific community
Contribute to shared AI platforms
and software libraries
applying state-of-the-art approaches including large language models
and multimodal methods to biological research questions
Contribute to open science by publishing code
support knowledge transfer across teams and sites through clear documentation
How You'll Work.
Team & Collaboration
work directly alongside experimental and computational scientists to understand their needs; work responsively across multiple projects and teams simultaneously; engage effectively with both technical and non-technical scientific stakeholders; contribute to a portfolio of pilot and flagship projects; providing shared engineering capacity across EMBL-AI; building the infrastructure, expertise, and community needed
Communication Scope
communication skills; clear documentation
Full Job Description
**Join us to shape**[** _EMBL AI_**](https://www.embl.org/topics/ai-at-embl/)**, 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: * Provi
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