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Doctolib

Machine Learning Engineer Intern (x/f/m)

Paris, France

The employer does not state the work arrangement.

Apply on the employer's siteOriginal listingJSON

PeopleSeek Talent does not submit applications. The button opens the employer's own page; this listing was verified 2 hours ago.

PayNot listed
Work arrangementNot stated
Who can applyNot stated by the employer
LevelIntern
TypeNot stated
Occupationmaintenance and repair engineer
Languageen
Posted26 October 2026
Last verified2 hours ago
StatusActive

About the role

We are looking for a Machine Learning Research Intern to join the Doctolib’s Artificial Intelligence teams.

As a Machine Learning Research Intern, your mission will be to contribute to developing AI solutions that transform access to healthcare and improve the experience of healthcare professionals and patients. You will work within a research team developing ethical AI technologies to optimize care pathways for millions of patients across Europe.

Working in the tech team at Doctolib involves building innovative products and features to improve the daily lives of care teams and patients. We work in feature teams in an agile environment, while collaborating with product, design, and business teams.

Your responsibilities include but are not limited to:

  • Develop and experiment with machine learning models to solve real-world healthcare challenges
  • Collaborate with ML engineers and researchers on applied research projects
  • Participate in improving medical practice efficiency through AI
  • Explore new model architectures and data processing techniques



We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here and learn about our first AI hackathon here!

You can also discover the topic you might work on during your internship here.

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 are a Master's student with practical experience working on ML problems
  • You are a strong programmer (we use Python)
  • You are enthusiastic about collaborative work
  • You are eager to ask questions, admit mistakes, and learn new things
  • You are interested in improving healthcare through technology
  • You are fluent in English and French

What we offer

  • A flexible workplace policy offering both hybrid and office-based mode
  • Lunch voucher with Swile card
  • Work Council subsidy to refund part of sport club membership or creative class
  • Reimbursement of public transportation

The interview process

  • A coding game online test
  • An interview with our Talent Partner (30 minutes)
  • A technical interview (1 hour)
  • An interview with the hiring manager (45 min)

Job details

  • Internship
  • Duration: 6 months
  • Start date: April 2027
  • Location: Levallois-Perret
  • Working mode: Hybrid - 3 days a week at the office

If you would like to find out more about tech life at Doctolib, feel free to read our latest Medium blog articles!

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, 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!

All information provided is processed by Doctolib for application management. For data processing details, click here.

Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.

Listed via Doctolib · first seen 26 October 2026 · last verified 28 October 2026

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