Senior Machine Learning Engineer Software engineering London

Checkout Ltd; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Checkout.com in London is seeking an experienced ML Engineer to join the Disputes ML team and help build our new ML-driven dispute optimisation suite. You will work on end-to-end features from data pipelines to model deployment, collaborating with platform and backend engineers to scale production systems.

Applicants should have 5+ years in ML/MLOps, strong Python, Databricks experience, and familiarity with AWS or Azure.

Qualifications

• 5+ years of experience in ML/MLOps or ML engineering.
• Proficient Python with production-quality code.
• Experience with production ML models (online or offline) and MLOps practices.
• Experience with monitoring and observability of production systems.
• Experience with training and operating models on Databricks.
• Familiarity with cloud-based application development (AWS & Azure).
• Familiarity with ML frameworks: scikit-learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton.

Responsibilities

• Build systems for training, deploying and monitoring ML models at scale for the Disputes platform.
• Develop and optimise data pipelines and backend services to process dispute and payment data in real time.
• Scale our feature store for online and offline use-cases.
• Own end-to-end feature delivery from requirements to production deployment.
• Turn raw data into production-ready features for dispute systems.
• Collaborate with platform and backend engineers to integrate models seamlessly.

Skills

Python

MLOps

Model training

Data pipelines

Feature store

Observability

Tools

Databricks

AWS

Azure

Spark

Kubeflow

Seldon

Job description

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. This is a unique opportunity to get in on the ground floor of an expanding area, grow alongside top‑tier engineers, and make a tangible impact on millions of disputes.

How you'll make an impact

• Build systems for training, deploying and monitoring machine learning models used in our Disputes platform, at scale.
• Build and optimise data pipelines and backend services to process dispute and payment data in real time.
• Build and scale our feature store for use‑cases both online and offline.
• Take 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 deployment.
• Turn raw data into production‑ready features that feed our dispute systems.
• Collaborate with platform and backend engineers to integrate models seamlessly.

Experience and qualifications

• 5+ years of experience as an MLOps / ML Engineer.
• High proficiency in writing clear, production‑ready Python code.
• Experience with production ML models (online or offline) and standard MLOps practices.
• Experience with monitoring and observability of production systems, with a strong sense of ownership.
• Experience with training and operating models on Databricks.
• Familiarity with cloud‑based application development (AWS & Azure).
• Familiarity with one or more ML frameworks and technologies: scikit‑learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton.
• Strong communication skills, able to express ideas clearly and collaborate across teams.