AI Engineer at EMBL

✨ The Stack take on this EMBL job

• 🏢 EMBL has 15 jobs here, 15 with visa sponsorship see all
• 🛂 Visa: EU Blue Card. Minimum salary: €50,700 a year guide

Updated 2026-11-05, from our data and official sources.

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About the role

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.

Responsibilities

You will provide shared AI engineering capacity to research groups by designing, deploying, and maintaining robust machine learning pipelines and infrastructure. The role acts as a bridge between cutting-edge AI research and practical scientific application within laboratory and computational workflows.

Requirements

Candidates must hold an advanced university degree in computer science, machine learning, or a related discipline and possess strong systems engineering experience. You must be able to design and evolve complete AI-enabled systems and communicate effectively with scientific stakeholders.

Key skills

• Machine learning — in 291 other listings on The Stack
• Deep learning — in 50 other listings on The Stack
• Systems engineering — in 3 other listings on The Stack
• Docker — in 464 other listings on The Stack
• Kubernetes — in 585 other listings on The Stack
• CI/CD pipelines — in 53 other listings on The Stack
• GPU infrastructure — in 6 other listings on The Stack
• Model serving — in 9 other listings on The Stack
• Large language models
• Computer vision
• Multimodal methods
• Python
• Distributed systems
• Observability tooling
• API development
• Scientific computing

On The Stack (as of 2026-11-05)

• EMBL has 14 other listings on The Stack, 14 of them marked as offering visa sponsorship.
• 166 visa-sponsored jobs in Germany on The Stack, posted in the last 60 days.
• 18 visa-sponsored ai engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about EMBL

Is this AI Engineer role still open?

It was posted on 2026-08-14. Listings more than 30 days old may no longer be active, so check EMBL's site before applying.

Does EMBL offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on EMBL's site.

Where is this role located?

The listing gives the location as Germany. It is marked on-site.

What is the salary for this role?

The listing states a salary of 4031.02.

What skills does this role ask for?

The listing names: Machine learning, Deep learning, Systems engineering, Docker, Kubernetes, CI/CD pipelines, GPU infrastructure, Model serving, Large language models, Computer vision, Multimodal methods, Python, Distributed systems, Observability tooling, API development, Scientific computing.