About the job
What You Will Do
At Doctolib, we are dedicated to revolutionizing healthcare delivery through the innovative use of artificial intelligence. As a Senior Machine Learning Engineer, you will be instrumental in creating and executing advanced AI solutions that enhance access to and quality of care for individuals everywhere.
This position allows you to collaborate with a diverse team of skilled Machine Learning Engineers, software developers, ML operations experts, and healthcare professionals to build and implement AI models that truly make a difference in people's lives.
Your work will focus on streamlining the process for patients to locate their healthcare providers, manage their long-term care plans, and assist our business team in expanding Doctolib's presence in various new markets.
We are seeking a Senior Machine Learning Engineer to join our dedicated team focused on patient solutions.
Your responsibilities will include, but are not limited to:
- Identifying the optimal technical solutions to meet product domain objectives
- Implementing and testing your innovative ideas
- Deploying algorithms in a production environment with guidance from our Machine Learning platform team
- Measuring performance improvements and continually refining your methodologies
Who You Are
You might be the perfect addition to our team if you:
- Possess strong analytical skills, are results-driven, and prioritize user experience
- Have a minimum of 7 years of experience in Machine Learning, Deep Learning, or AI Engineering, with proven success in transitioning models from prototype to large-scale production
- Have extensive expertise in Information Retrieval and modern retrieval technologies, including:
- Hybrid search methods (sparse + dense)
- Large-scale embeddings and vector databases
- Multi-stage retrieval and re-ranking systems
- RAG architectures and retrieval processes for multimodal use cases
- Tool-based integrations for incorporating external data and functionalities
- Are proficient in developing applications using LLM/VLM, including:
- Fine-tuning LLM and VLM models
- Implementing Mixture-of-Experts (MoE) architectures
- Applying Knowledge Distillation techniques
- Engaging in prompt engineering and tool utilization
- Evaluating and benchmarking LLM/VLM systems
- Have hands-on experience with agentic AI, including constructing and orchestrating agents based on ADK frameworks
