Reality Defender - Staff Data Platform Engineer

• Own the end-to-end data platform for AI research: continuous ingestion, preprocessing, governance, and export of image, video, audio, and text data at high volume. • Complete and scale our Databricks lakehouse and distributed processing stack (Spark, Ray, or similar), making architecture and build-vs-buy decisions that hold up as data volume grows. • Build reusable infrastructure for in-house synthetic data generation, so researchers can produce new deepfake variants across modalities on demand instead of one-off. • Make every dataset reproducible and traceable through versioning, lineage, metadata, quality checks, and stable train/eval splits, so any model result can be tied back to the exact data behind it. • Own pipeline reliability, observability, and processing cost per dataset as core metrics of the platform. • Partner with ML researchers to understand their data needs and turn them into self-serve tools that speed up model iteration. • Mentor engineers and raise the bar on data engineering practices: code review, testing, documentation, and runbooks. • Work with Security and compliance stakeholders to make sure data handling, licensing, and access controls meet the standards our enterprise and government clients expect. • Help shape the long-term data platform roadmap with engineering and research leadership. • Own a mature, self-serve data platform that researchers use to create, version, and export datasets without waiting on platform support. • Have scaled synthetic data generation across vision, audio, and multimodal research, broadening our detection coverage of new generative techniques. • Deliver reproducible evaluation datasets that let the company compare model versions with confidence. • Be setting the multi-year data platform roadmap and leading high-leverage initiatives beyond the original scope.