AI Engineer at EMBL

on-site · full time · 4031.02 · Visa sponsorship

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Responsibilities:
- Provide shared AI engineering capacity across EMBL: prototype, integrate, and operationalise AI models (LLMs, computer vision, multimodal) into laboratory and computational workflows.
- Design, build and maintain ML/DL pipelines and infrastructure: training, evaluation, versioning, CI/CD, model serving, and reproducibility.
- Operate and optimise GPU-enabled compute stacks and containerised deployments (Docker, Kubernetes), and integrate with European AI compute ecosystems.
- Develop APIs, shared libraries and platform services; implement monitoring, logging and observability to ensure reliable model behaviour in production.
- Work as a "lab in the loop": collaborate directly with experimental and computational scientists to translate requirements into usable tools, documentation and training materials.
- Contribute to open science: publish code, models and benchmarks; support knowledge transfer across teams and sites.

Requirements:
- Advanced university degree in computer science, machine learning, mathematics, computational biology or related discipline.
- Proven experience (typical 5+ years) building and deploying ML/AI systems in research or production environments.
- Strong Python and ML/DL skills; experience with large language models, computer vision or multimodal methods is desirable.
- Systems and MLOps expertise: Docker, Kubernetes, CI/CD, model serving, GPU infrastructure, distributed systems and observability tooling.
- Familiarity with scientific computing workflows, data management best practices and open science principles.
- Excellent collaboration and communication skills; ability to work closely with diverse scientific teams and translate technical solutions into research impact.