Senior/Staff Machine Learning Engineer - Health Evaluation - AI Teams (x/f/m)
DoctolibParis, Paris, FrancePosted 30 March 2026
Job Description
What you’ll do
At Doctolib, we're on a mission to transform how healthcare is delivered by harnessing the power of AI.
As a Senior/Staff Machine Learning Engineer, you’ll play a key role in designing, implementing, and scaling the evaluation framework that ensures our AI Health Companion behaves safely, reliably, and helpfully for millions of patients and practitioners.
You’ll join a cross-functional team of Machine Learning Engineers, Product Engineers, and Medical Experts to build robust evaluation pipelines for agentic AI systems — models capable of reasoning, planning, and interacting with complex healthcare data.
Your responsibilities include, but are not limited to:
Define and own the evaluation strategy for our AI agentic system - metrics, protocols, datasets, and tooling
Implement and maintain automated evaluation pipelines to monitor model quality, safety, and alignment across iterations
Run systematic experiments to assess reasoning, factuality, robustness, and user experience
Collaborate closely with model developers and research scientists to provide insights and drive iterative improvement
Contribute to research and internal knowledge sharing on LLM evaluation methodologies and best practices
About our tech environment
Our solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to the country and healthcare specialty requirements. To address these challenges, we are modularizing our platform run in a distributed architecture through reusable components
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 !
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.
MSc or PhD in Computer Science, Machine Learning, Data Science, or related field
7+ years of hands-on experience working with large language models (e.g., GPT, Claude, Llama, or BERT-like architectures)
Proven experience in evaluating agentic or reasoning systems (e.g., autonomous agents, tool-using LLMs, dialogue systems, or task-oriented assistants)
Strong track record in experiment design, metric definition, and evaluation automation
Ability to bridge research and production, influencing modeling and product decisions
Excellent communication skills and a collaborative mindset
Now it would be fantastic if:
You have experience in the clinical or medical domain and sensitivity to ethical or regulatory challenges in healthcare AI
What we offer
Free health insurance for you and your children
Parent Care Program: receive one additional month of leave on top of the legal parental leave
Free mental health and coaching services through our partner Moka.care
For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
Work from EU countries and the UK for up to 10 days per year, thanks to our flexibility days policy
Work Council subsidy to refund part of sport club membership or creative class
Up to 14 days of RTT
Lunch voucher with Swile card
The interview process
Recruiter interview
Technical Deep Dive
Data System Design
Behavioral Interview
At least one reference check
Job details
Permanent position
Full Time
Workplace : Hybrid in our Levallois office
Start date: asap
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, a ... (truncated, view full listing at source)
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