Staff Engineer, Machine Learning & Data Infrastructure

Lead the architecture and hands-on development of ML and data infrastructure for risk and fraud prevention systems. Responsibilities include improving model inference performance, building reusable model deployment infrastructure, designing offline data pipelines on Databricks and Spark, connecting offline and online feature pipelines, and setting technical direction while mentoring engineers.

Proven experience building large-scale distributed systems, ML infrastructure, or data platforms. Hands-on optimization of production model performance. Deep knowledge of Databricks/Spark. Experience with model lifecycle (training/serving, validation, deployment). Strong Python, SQL, and a backend language (Java, Scala, Go, C++). Ability to lead technical direction. Preferred: SageMaker, ONNX Runtime, TensorRT, Delta Lake, MLflow, Kafka, Flink, fraud detection, Mandarin.