Machine Learning Engineer

Take recommendation models from notebook to production serving millions of users in real time.

Paris France Permanent Hybrid working policy to be confirmed

• Building and maintaining recommendation and ranking models used across core product surfaces
• Owning the ML lifecycle from feature preparation and training to deployment and production monitoring
• Designing offline evaluation methods that reflect the behaviour expected in production
• Building training and retraining pipelines that can run reliably without manual intervention
• Deploying models through scalable low-latency production services
• Designing A/B tests and defining the metrics used to assess model performance and business impact
• Working with data engineers to make production features reliable consistent and available at the required frequency
• Monitoring model performance feature quality drift latency and prediction distributions
• Investigating differences between offline results and production behaviour
• Improving model deployment versioning rollback and reproducibility
• Partnering with product teams to translate business objectives into measurable optimisation problems
• Contributing to code reviews automated testing CI/CD and ML engineering standards

• Serving real-time features and predictions within strict latency requirements
• Maintaining consistency between offline training data and online production features
• Automating retraining pipelines while keeping model versions datasets and experiments reproducible
• Detecting model or feature drift before it has a significant impact on users
• Designing A/B tests with appropriate metrics sample sizes and evaluation periods
• Balancing model complexity and prediction quality against latency and infrastructure costs
• Handling cold-start problems for new users products or categories
• Rolling out new models safely with clear monitoring and rollback mechanisms

• 3 years of experience deploying and operating machine learning models in production
• Strong Python skills and good software engineering practices
• Practical experience with PyTorch TensorFlow or an equivalent ML framework
• Experience building training evaluation and inference pipelines
• Knowledge of recommendation ranking personalisation or similar machine learning systems
• Experience with model deployment monitoring versioning and automated retraining
• Understanding of feature engineering and the differences between offline and online feature processing
• Experience designing or analysing controlled experiments and A/B tests
• Ability to investigate performance issues across models data pipelines and production infrastructure
• Experience using Git code reviews automated testing and CI/CD
• Confidence working with data engineering platform and product teams

You do not need to have worked with every tool in the stack but you should have experience taking models beyond experimentation and operating them as production systems.

A fast-growing digital marketplace with millions of monthly active users several hundred employees and a strong presence across Europe.

Machine learning is used across key product areas including recommendation ranking and personalisation. The team is developing the infrastructure and engineering practices required to operate these models reliably at scale.

Health insurance meal vouchers and an equity plan.

Languages: Native or bilingual French and professional English.

A search run by The French Sourcer recruitment built for technical teams.