As an ML (Machine Learning) Engineer at 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.