At a Glance
- Tasks: Develop and optimise ML systems for real-time dispute processing.
- Company: Checkout.com, a leading tech company in the financial sector.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic startup-like environment with excellent career advancement potential.
- Why this job: Join a pioneering team and make a real impact on millions of disputes.
- Qualifications: 5+ years in MLOps/ML Engineering with strong Python skills.
The predicted salary is between 70000 - 90000 £ per year.
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
