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

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> Responsibilities: - Design and implement ML/AI solutions for

- **Company:** Doctolib (https://jobs.trythestack.co/companies/doctolib)
- **Location:** Paris, FR, France
- **Work arrangement:** Hybrid
- **Employment type:** Full-time
- **Experience level:** 5–10 years
- **Visa sponsorship:** Offered, according to the listing
- **Key skills:** 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
- **Posted by employer:** 2026-11-02
- **Added to The Stack:** 2026-11-03
- **Listed until:** 2027-01-01
- **Status:** Active
- **Apply:** https://jobs.ashbyhq.com/doctolib/26543700-22d5-47c7-9ad5-3bdbdae5e7d4?src=LinkedIn (the employer's own application page)
- **Human page:** https://jobs.trythestack.co/jobs/senior-machine-learning-engineer-applied-ai-llms-xfm-doctolib

## 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.

## FAQ

- **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.

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## Notes for agents

- Apply on the employer's page (https://jobs.ashbyhq.com/doctolib/26543700-22d5-47c7-9ad5-3bdbdae5e7d4?src=LinkedIn). The Stack does not take applications.
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