Senior Machine Learning Engineer - Applied AI & LLMs (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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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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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

Cover letter

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Assessment

Required From the posting source checked Jun 20, 2026

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

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

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