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

Doctolib is hiring a Senior Machine Learning Engineer - Applied AI & LLMs (x/f/m) in Paris, France. Level rates it ; you can apply on Doctolib.

Level's read on this job

On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
Some of the work can be done remotely, according to the posting.
The posting mentions MLflow, Python and PyTorch among its tools and skills.
Level classes it as a senior role.
Doctolib has 155 other open roles on Level.

AI in this role

Build and scale advanced machine learning systems, LLM applications, and retrieval pipelines to improve patient care access at Doctolib.

mlflowpythonpytorchvector-databasesllmsweights-and-biases

prompt-engineeringragfine-tuningmachine-learningdeep-learninginformation-retrievalmlops

Your Impact

We are looking for a Senior Machine Learning Engineer to join the ML Engineering team in Patient Solutions. Your mission will be to improve how people access quality care and manage their health over time by building and leading AI and ML systems that create real, measurable impact. You will work in a feature team developing intelligent patient-facing solutions, from smart practitioner discovery to long-term care management, playing a key technical role in shaping how we scale our AI capabilities across Europe. Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients.

What you'll do

Your responsibilities include but are not limited to:

Design and implement ML and AI solutions aligned with patient product goals, covering search, retrieval, and personalized care pathways
Build and maintain large-scale retrieval pipelines, including hybrid search, embedding systems, vector databases, and multi-stage re-ranking architectures
Develop, fine-tune, and evaluate LLM and VLM models using techniques such as knowledge distillation, Mixture-of-Experts (MoE) architectures, and prompt engineering
Build and orchestrate agentic AI systems, integrating external data and capabilities through tools and MCP-based integrations
Define metrics aligned with product goals, run controlled end-to-end experiments using W&B, MLFlow, or Braintrust, and communicate findings to guide product and technical decisions
Deploy solutions to production in collaboration with our ML platform team, ensuring reliability, observability, and performance at scale, and act as a technical reference to elevate the team's standards and practices

Who you are

Before you read on: if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply. You'll be a great fit if you:

You have 7+ years of experience in Machine Learning, Deep Learning, or AI Engineering, with a strong track record of taking models from prototype to production at scale
You have strong experience in Information Retrieval and modern retrieval stacks: hybrid search (sparse + dense), large-scale embeddings and vector databases, multi-stage retrieval and re-ranking pipelines, RAG architectures, and tool/MCP-based integrations
You are proficient in LLM and VLM application development: fine-tuning, MoE architectures (via LiteLLM or Model Garden), knowledge distillation, prompt engineering, and systematic benchmarking of LLM/VLM systems
You have hands-on experience building and orchestrating agentic AI systems (e.g., using ADK)
You demonstrate strong scientific rigor: designing metrics aligned with product goals, running controlled experiments, and communicating results clearly to both product and engineering stakeholders
You have experience operating large-scale applications in production (monitoring, reliability, performance, observability), bring strong analytical skills, and approach your work with a user-first mindset. You are fluent in English

It would be fantastic if you:

Have experience in B2C marketplace environments
Have experience in other ML methodologies: pattern mining, recommendation systems, experimentation, or causal inference

Life at Doctolib Tech

Our solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.
Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.
We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here.

Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.

What we offer

Free comprehensive health insurance (basic package) for you and your children
25 days of paid vacation per year, plus up to 14 days of RTT
Free mental health and coaching services through our partner Moka.care
Work from abroad for up to 10 days per year thanks to our flexibility days policy
Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib
A subsidy from the work council to refund part of the membership to a sport club or a creative class
50% reimbursement of your public transport subscription
ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib
Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth
For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
Relocation support in case of international mobility
Access to the best AI tools for coding, development and dedicated training

Our interview process

Recruiter Interview
Feature Building Interview
System Design Interview
Behavioral Interview
At least one reference check

We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.

Job details

Permanent position
Full-time
Location: Doctolib Paris office in Levallois-Perret
Hybrid work setup (3 days/week in the office)
Start date: as soon as possible

We welcome everyone

At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination. The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, or disability. To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process. Join us in building the healthcare we all dream of!

Your data privacy

All information provided is processed by Doctolib for application management. For data processing details, click here: France. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.

How we rate this

Senior Machine Learning Engineer - Applied AI & LLMs (x/f/m) at Doctolib rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Machine Learning Engineer - Applied AI & LLMs (x/f/m) is an open job at Doctolib.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Paris, Paris, France and is marked remote.
The job was posted on June 1, 2026.
Seniority: senior.
Applications are handled by Doctolib. Apply on Doctolib

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGFine-tuningMachine learningDeep learningInformation RetrievalMLOpsMLflow

Also common in machine learning engineer jobs at this level

PyTorch TensorFlow AWS Generative AI C++Kubernetes

Pay

Typical machine learning engineer job: $199k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, Fine-tuning, Machine learning, and Deep learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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