• Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience). • Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability. • Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives). • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls. • Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability. • Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability. • Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements. • Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team.