Senior Machine Learning Engineer - AI in London

You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we're behind many of the digital experiences you use every day. Whether you want to book a holiday, order food, renew a subscription, or check out online, there's a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. As an ML (Machine Learning) Engineer at Checkout.com in the Disputes ML team, you will contribute to the development of our brand-new, ML-driven dispute optimisation suite. You will be building solutions that power our stack of value added services in the Disputes area. We're a growing team in an expanding area within the company and we're looking for individuals who have strong ownership, are passionate about productionising ML and have a pragmatic approach to converting big problems into smaller iterations to constantly deliver value. How you'll make an impactBuild systems for training, deploying and monitoring machine learning models used in our Disputes platform, at scaleBuild and optimize data pipelines and backend services to process dispute and payment data in real timeBuild and scale our feature store for use-cases both online and offlineTake complete ownership of delivering comprehensive, end-to-end features within a startup-like setting, driving the entire lifecycle from requirement refinement, data pipeline construction and model training to troubleshooting and production deploymentTurn raw data into production-ready features that feed our dispute systemsCollaborate with platform and backend engineers to integrate models seamlesslyExperience and qualifications5+ years of experience as MLOps /ML EngineerHigh proficiency in writing clear, production-ready Python codeExperience with production ML models (online or offline) and standard MLOps practicesExperience with monitoring and observability of production systems, with a strong sense of ownershipExperience with training and operating models on DatabricksFamiliarity in Cloud-based application development (we use AWS & Azure)Familiarity with one or more ML frameworks and technologies: scikit-learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, TritonStrong communication skills, able to express ideas clearly and collaborate across teamsAdditional InformationBring all of you to workWe create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.