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
• Within 12 months, you’ll…
• 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

Benefits

• Healthcare plans with 100% premium coverage for employees and partial coverage available for dependents
• Dental and Vision plans with 100% premium coverage for employees and their dependents
• Short/Long-term disability and life insurance plans with 100% premium coverage for employees
• FSA/HSA and 401k programs
• 12 weeks of Parental Leave
• Learning and Development budget
• 20 days of PTO per year
• Equity compensation
• Annual company-sponsored offsite
• Monthly wellness benefits

• Experience building synthetic data generation pipelines or running generative models (diffusion, voice cloning/TTS, video generation) at scale. (nice to have)
• Deep hands-on experience with distributed processing (Spark, Ray, or similar) and lakehouse platforms, ideally Databricks, on top of cloud object storage (S3, GCS, or Azure Blob)
• 8+ years building data platforms or data infrastructure, including staff- or lead-level ownership of production pipelines that generate, ingest, or deliver data at high volume
• Background supporting ML research teams with training and evaluation data, especially for multimodal models. (nice to have)
• A track record of building reproducible ML datasets: versioning, lineage, data quality validation, and orchestration (Airflow, Dagster, Databricks Workflows, or similar)
• Production experience with Databricks, Unity Catalog, Delta Lake, and Spark on AWS, including performance tuning, access controls, and operational troubleshooting
• Own infrastructure and deployment automation using Terraform and CI/CD, including AWS storage, IAM, compute, Databricks resources, and distributed processing environments
• Experience working with large unstructured media such as video, audio, or images, including the realities of large files, decoding and transcoding, and metadata at scale
• Experience in security, fraud, trust & safety, or another adversarial domain, or at a Series A or B startup where scope regularly outpaces headcount. (nice to have)
• Strong Python and SQL, and the judgment to own ambiguous platform problems end-to-end on a lean team