Design and implement ML and AI solutions for search, retrieval, and personalized care pathways
Build and maintain large-scale retrieval pipelines with hybrid search and re-ranking
Develop, fine-tune, and evaluate LLM and VLM models
Build and orchestrate agentic AI systems with tool and MCP integrations
Run controlled end-to-end experiments and communicate findings to guide decisions
From the employer’s posting
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
run controlled end-to-end experiments using W&B, MLFlow, or Braintrust, and communicate findings to guide product and technical decisions
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 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.
Qualification wording
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 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
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
ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib
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
Our interview process
Recruiter Interview
Feature Building Interview
System Design Interview
Behavioral Interview
At least one reference check
Job details
Permanent position
Full-time
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!
Open 5 monthsWe first saw this posting 157 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
Status in our records
Live
First seen by us
Jun 1, 2026157 days ago
Recorded sightings
155
Last seen by us
Nov 2, 2026
Jun 1, 2026today
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 157
670
Up longer than most. Similar jobs usually come down between day 6 and day 70.
Well past Doctolib’s usual: its jobs come down by about day 15.
From 4,448 similar postings that came down, and 14 of Doctolib’s. Coming down is not always a hire.
Doctolib is hiring
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Java
Kotlin
Python
TypeScript
MLflow
React
Where the posting names them
- 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.
- 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
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
August 17Last week
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Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
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.
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Pay
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Location & working pattern
Paris, Paris, France
- 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
More source context
- 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
More relevant text appears in the full description.
Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.