Principal Machine Learning Engineer - Applied AI & LLMs (x/f/m) at Doctolib

✨ The Stack take on this Doctolib job

• 🏢 This is Doctolib's only job here right now
• 📊 4 Machine Learning Engineer jobs in Germany in the last 60 days see them
• 🛂 Visa: EU Blue Card. Minimum salary: €50,700 a year guide
• 💡 No salary shown. Ask Doctolib if it is above the minimum.

Updated 2026-11-01, from our data and official sources.

About the role

Responsibilities:
- Define and own cross-team ML architecture decisions: model selection, deployment patterns, build vs. buy trade-offs.
- Design and propagate evaluation frameworks and metrics for LLMs, agents and production models across teams.
- Establish shared production patterns: model/versioning, prompt/version control, guardrails, uncertainty quantification, cost management and observability.
- Lead technical design of agentic solutions, guide integrations with product features and clinical workflows.
- Mentor and elevate Staff/Senior engineers through architecture reviews, best practices and hands-on technical leadership.
- Partner with Product, Legal and Compliance to ensure safe, auditable rollouts meeting GDPR, MDR and EU AI Act requirements.

Requirements:
- 10+ years in ML/AI with 3+ years at Staff/Principal level or equivalent leadership experience.
- Deep expertise in at least two domains: clinical NLP, LLM fine-tuning/evaluation, ASR, RAG systems or RL.
- Strong engineering skills in Python and PyTorch/Transformers; experience with vLLM or high-throughput inference systems and cloud deployments (AWS/GCP).
- Track record deploying and operationalising ML systems in production, including monitoring, versioning and cost/control measures.
- PhD or equivalent research experience preferred; excellent communication and stakeholder alignment skills.
- Nice-to-have: publications in top ML venues, prior experience with EU healthcare regulations or clinical data.

Responsibilities

Own cross-team ML architecture decisions, from model selection to production deployment patterns, and define agentic AI solutions. Establish and propagate rigorous evaluation frameworks and standardized production infrastructure for AI. Lead architecture reviews and guidance across the DS & AI org to ensure safe rollout with regulatory compliance.

Requirements

10+ years in ML/AI with 3+ years at Staff+ or Principal level; deep expertise in at least two domains (e.g., NLP, LLM fine-tuning, ASR, or RL). Strong Python, PyTorch/Transformers, and vLLM experience with production deployments (AWS/GCP). PhD or equivalent research experience plus exceptional communication.

Key skills

• Python — in 1142 other listings on The Stack
• PyTorch — in 87 other listings on The Stack
• Transformers — in 6 other listings on The Stack
• vLLM — in 8 other listings on The Stack
• AWS — in 776 other listings on The Stack
• GCP — in 163 other listings on The Stack
• Natural Language Processing — in 17 other listings on The Stack
• LLM fine-tuning — in 3 other listings on The Stack
• Automatic Speech Recognition
• RAG systems
• Reinforcement Learning
• Production ML deployment

On The Stack (as of 2026-11-01)

• This is Doctolib's only listing on The Stack.
• 166 visa-sponsored jobs in Germany on The Stack, posted in the last 60 days.
• 27 visa-sponsored machine learning engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Doctolib

Is this Principal Machine Learning Engineer - Applied AI & LLMs (x/f/m) role still open?

It was posted on 2026-11-01 and is listed as active on The Stack. Confirm on Doctolib's application page before applying.

Does Doctolib offer visa sponsorship?

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

Where is this role located?

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

What skills does this role ask for?

The listing names: Python, PyTorch, Transformers, vLLM, AWS, GCP, Natural Language Processing, LLM fine-tuning, Automatic Speech Recognition, RAG systems, Reinforcement Learning, Production ML deployment.