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

What you’ll do

At Doctolib, we're on a mission to transform the way healthcare is delivered by leveraging the power of AI. As a Senior Machine Learning Engineer, you'll play a critical role in developing and implementing cutting-edge AI solutions that will simplify the access and quality of care for all.

In this role, you'll have the opportunity to work with a team of talented Machine Learning Engineers, software engineers, ML ops and healthcare professionals to develop and deploy AI models that will have a real impact on people's lives.

You will work at simplifying how patients find their healthcare practitioner, handle their care plan in the long term and work with business team to contribute expansion of Doctolib in several new markets.

Doctolib is looking for a Senior Machine Learning Engineer to join our Machine Learning Engineer team in charge of our patient solutions.

Your responsibilities include but are not limited to:

Find the right technical solution to solve the product domain goals

Implement your ideas and test them

Deploy your algorithms in production guided by our ML platform team

Measure the uplift and continuously improve your approach

Who you are

You could be our next team mate if you:

Have strong analytical skills, are result oriented and user first

Have 7 years of experience in the domain of Machine Learning / Deep Learning / AI Engineering, including taking models from prototype to production at scale.

Have deep experience in Information Retrieval and modern retrieval stacks, including some of the following:

Hybrid search (sparse + dense)

Large-scale embeddings and vector databases

Multi-stage retrieval and re-ranking pipelines

RAG architectures and retrieval pipelines on multimodal use cases

Tool- and MCP-based integrations to bring in external data and capabilities

Proficient in LLM/VLM application development, including:

Fine-tuning LLM and VLM models

Mixture-of-Experts (MoE) architectures (via LiteLLM or Model Garden)

Knowledge Distillation

Prompt engineering and tool use

Evaluation and benchmarking of LLM/VLM systems

Have hands-on experience with agentic AI (e.g. building and orchestrating agents on top of ADK).

Have demonstrated strong scientific rigor and benchmarking skills:

Designing metrics aligned with product goals,

Running controlled end to end experiments with W&B, MLFlow or Braintrust

Analysing and communicating results to guide product and technical decisions.

Have experience with large scale applications in production (monitoring, reliability, performance, observability).

Now, it would be fantastic if you:

Have experience in B2C marketplace.

Have experience in other ML methodologies : pattern mining, recommendation, experimentation or causal inference.