We are backed by top investors and industry leaders, serving clients across Europe, the United States, Latin America, Asia, and the Middle East. Together, we are revolutionizing the insurance claims landscape with AI-driven solutions.

About the Role

Job Title: Machine Learning Engineer
Location: Paris
Employment Type: Full-time
Department: unspecified

You will shape Qantev’s next-generation AI models, from information extraction to anomaly detection. Your work will power document understanding, medical code inference, and scalable fraud detection architectures. You will occupy a central position in the model development lifecycle, writing high-quality production code for our inference server, aligning closely with the product and platform teams, and designing automated evaluation pipelines to continuously assess and improve performance.

Responsibilities

• Model Design: Architect and develop deep learning models for NLP (transformers, VLMs), anomaly detection (GNNs, autoencoders), and UI-L integration.
• Custom Solutions: Build and fine-tune domain-specific LLMs or vision-language models for document OCR, field extraction, and medical inference.
• Scalable Infrastructure: Design model-serving pipelines, considering batching, sharding, quantization, and monitoring.
• Continuous Learning: Implement active learning and feedback loops to retrain models based on investigator annotations.
• Performance Analysis: Define and track precision, recall, NGCD, and other metrics; conduct A/B tests and rule simulations.
• Collaboration: Work closely with MLOps, data engineers, and product teams to deploy models in production and iterate rapidly.

Required Qualifications

• 5+ years in machine learning or deep learning engineering.
• Expert in PyTorch or TensorFlow; experience with the Hugging Face ecosystem.
• Strong background in NLP, vision-language models, and graph neural networks.
• Familiarity with model optimization techniques (FT, LoRA, quantization, pruning).
• Solid understanding of MLOps: containerization, monitoring, and CI/CD for ML.
• Excellent communication and documentation skills.

Nice-to-Have

• Experience in healthcare or insurance ML applications.
• Publications in top-tier ML/NLP conferences.
• Knowledge of Bayesian re-ranking, self-supervised learning, or agentic autoML frameworks.

Recruitment Process

• Screening Interview: A brief initial conversation to understand your background and interests.
• Machine Learning Interview: A deep dive into your technical expertise in ML, including model building and evaluation.
• System Design Interview: Assess your ability to design scalable and maintainable ML systems and pipelines.