Member of Technical Staff, Data Infrastructure

Member of Technical Staff, Data Infrastructure We are building AI to simulate the world through merging art and science. We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world. World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached. Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you. ABOUT THE ROLE We're looking for a Data Engineer to build and scale the data infrastructure that powers Runway's AI research and business intelligence. You'll own critical data pipelines spanning production databases, analytics warehouses, and large-scale ML training datasets. This role sits at the intersection of data engineering, ML infrastructure, and analytics—you'll enable both world-class research and data-driven business decisions. You'll work on challenging problems at scale: managing billions of rows of multimodal training data, building CDC streams from production systems, optimizing vector databases for ML workflows, and creating the foundational data layer that the entire company relies on. A PEEK AT OUR TECHNICAL STACK Our data infrastructure spans multiple specialized systems: LanceDB for vector storage and dataset versioning with multimodal training data, ClickHouse as our analytics warehouse receiving CDC streams from production Postgres via AWS Kinesis, and BigQuery for training run logs and evaluation results. We use Ray for large-scale distributed data processing on managed Kubernetes clusters, handling preprocessing, feature generation, and dataset curation at scale. We're actively building out our data platform—introducing dbt for standardized transformations, improving dataset versioning and data lineage tracking, scaling data sourcing pipelines, and establishing better data quality practices. We use Prometheus and Grafana for monitoring, and Terraform for infrastructure management. This is an opportunity to bring best practices and technical leadership as we mature our data infrastructure to support rapidly growing ML training and research needs. WHAT YOU’LL DO - Build and own pipelines for the creation, curation, and processing of large-scale multimodal datasets, including vector database (LanceDB) management and query optimization for ML metadata - Build and own ETL and CDC streams from Postgres and ClickHouse to analytics warehouses - Build standardized data transformation layers using dbt to replace ad-hoc SQL queries and create maintainable data models for business analytics - Manage production databases (Postgres, ClickHouse) and optimize for performance and reliability WHAT YOU’LL NEED - 4+ years of industry experience in data engineering - Strong knowledge of Python - Experience with data quality, deduplication, and cleaning at scale - Comfortable working with cloud storage (S3) and managing large datasets - Experience building and maintaining ETL/CDC pipelines at scale - Strong SQL skills and experience with multiple database systems (Postgres, columnar databases like ClickHouse/Redshift) - Humility and open mindedness; at Runway we love to learn from one another NICE TO HAVE - Experience with one or more frameworks for large-scale data processing (e.g. Spark, Ray, etc) and one or more ML frameworks (e.g. PyTorch, JAX) - Knowledge of cloud platforms (AWS, GCP, or Azu ... (truncated, view full listing at source)