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

✨ The Stack take on this Doctolib job

• 🏢 Doctolib has 4 jobs here, 4 with visa sponsorship see all
• 📊 2 Machine Learning Engineer jobs in France in the last 60 days see them
• 🛂 Visa: Talent passport. EU Blue Card salary: €59,373 a year (1.5 times the average salary) guide
• 💡 No salary shown. Ask Doctolib if it is above the minimum.

Updated 2026-11-03, from our data and official sources.

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About the role

Responsibilities:
- Design and implement ML/AI solutions for patient-facing features (search, retrieval, personalized care pathways).
- Build and maintain large-scale retrieval pipelines: hybrid search, embedding creation, vector DBs, multi-stage re-ranking and RAG flows.
- Develop, fine-tune and evaluate LLM and VLM models; apply distillation, MoE patterns and prompt engineering where appropriate.
- Architect and orchestrate agentic AI systems integrating external tools and data sources; ensure safe-by-default behavior.
- Define product-aligned metrics and run controlled experiments (W&B, MLflow or similar); translate results into product and technical decisions.
- Deploy and operate solutions with the ML platform team, focusing on reliability, observability and performance at scale; act as a technical reference.

Requirements:
- 7+ years in ML/AI with a track record of taking models from prototype to production at scale.
- Strong expertise in information retrieval and modern retrieval stacks (sparse+dense, vector DBs, re-ranking, RAG).
- Hands‑on experience with LLM/VLM development, fine-tuning, evaluation, MoE or distillation techniques and prompt engineering.
- Experience building agentic AI/assistant architectures and integrating toolchains/MCPs.
- Practiced in experiment design, metrics definition and communicating results to product and engineering stakeholders.
- Experience operating production ML systems (monitoring, observability, reliability) and fluent English.

Responsibilities

Design and implement ML/AI solutions for patient-facing features and scalable AI capabilities across Europe. Build and maintain large-scale retrieval pipelines, integrate LLM/VLM models, and develop agentic AI systems. Collaborate with ML platform and product teams to ensure reliability, observability, and impact.

Requirements

7+ years in ML/AI with experience taking models to production; strong information retrieval and vector database expertise; hands-on LLM/VLM development and evaluation; experience with production systems, monitoring, and experimentation; fluent English.

Key skills

• Information Retrieval — in 3 other listings on The Stack
• Hybrid search — not in any other listing on The Stack
• Vector databases — in 13 other listings on The Stack
• Large Language Models (LLMs) — not in any other listing on The Stack
• Vision-Language Models (VLMs) — not in any other listing on The Stack
• Knowledge Distillation — not in any other listing on The Stack
• Mixture-of-Experts (MoE) — not in any other listing on The Stack
• Prompt Engineering — in 56 other listings on The Stack
• Agentic AI systems
• RAG architectures
• MLOps
• Observability

On The Stack (as of 2026-11-03)

• Doctolib has 3 other listings on The Stack, 3 of them marked as offering visa sponsorship.
• 25 visa-sponsored jobs in France on The Stack, posted in the last 60 days.
• 29 visa-sponsored machine learning engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Doctolib

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

It was posted on 2026-11-02 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 Paris, FR, France. It is marked hybrid.

What skills does this role ask for?

The listing names: Information Retrieval, Hybrid search, Vector databases, Large Language Models (LLMs), Vision-Language Models (VLMs), Knowledge Distillation, Mixture-of-Experts (MoE), Prompt Engineering, Agentic AI systems, RAG architectures, MLOps, Observability.