Senior/Staff Machine Learning Engineer - Health Evaluation - AI Teams (x/f/m)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior/Staff Machine Learning Engineer - Health Evaluation - AI Teams (x/f/m) Paris, Paris, France 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,

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