Machine Learning Engineer at Korapay

Role Summary

• We run payments across Africa and are now positioned as a global fiat and stablecoin payment infrastructure. We offer mobile money, virtual bank accounts, and virtual cards for payins and payouts across multiple markets. Our data infrastructure is batch-first (Airflow + a cloud data warehouse) and we use Vertex AI for our MLOps lifecycle. The ML team is high-ownership: you will build models, design systems, ship them, and observe them in production.
• You will work on merchant-facing intelligence: forecasting, anomaly detection, segmentation, as well as automation and product-layer ML. If you want to build practical things that matter in a context that most ML engineers never get near, this is the role.

What You'll Work On

• Design and ship a per-merchant payment volume forecasting system: time-series decomposition, Africa-specific event calendars (salary cycles, MNO maintenance windows, public holidays), quantile regression for uncertainty bounds
• Build and maintain fraud/ anomaly detection across the payment stack (residual-based and model-driven) with tiered alerting logic mapped to merchant risk profiles.
• Own the dynamic merchant segmentation system end-to-end: rule-based and data-driven hybrid, percentile thresholds grounded in EDA, segment-transition features as ML inputs
• Instrument and monitor deployed models: drift detection, retraining triggers, and evaluation pipelines via Vertex AI
• Build automation tooling that sits alongside the core ML work: Airflow DAGs, pipeline scaffolding, and tooling to reduce operational toil
• Contribute to product and strategic thinking.

Requirements

Our Stack

• Apache Spark and Airflow
• Google Vertex AI
• Python
• SQL
• GCS/BigQuery

What We're Looking For

• 3+ years as an ML engineer in a production environment
• Strong Python and comfort with Spark for large-scale data processing
• Experience with time-series modelling: decomposition, forecasting, anomaly detection
• Solid grasp of the ML lifecycle as a unified discipline
• Ability to work with batch infrastructure and design for it deliberately
• High ownership mentality: you notice problems and fix them as opposed to waiting to be assigned
• Ability to identify gaps in data-driven business processes and come up with solutions