Senior AI/ML Engineer

What You'll Do

Build and operate ML-ready data systems. Create the data preparation, feature generation, and training pipelines that AI researchers and ML engineers use to take models from experiment to production. Own data versioning, validation, and reproducibility for ML workflows.

Deploy and support production AI/ML systems. Build and maintain the pipelines that serve model training, testing, validation, deployment, and inference in production. Partner with ML engineers to operationalize models reliably.

Own data systems end-to-end. Design, build, and operate ETL/ELT pipelines that ingest data from real-time event streams, third-party APIs, and media-rich sources into our lakehouse architecture. Own throughput, latency, reliability, and cost.

Run production workloads on Kubernetes. Deploy, monitor, and troubleshoot containerized data services and distributed processing applications in cloud-native Kubernetes environments.

Develop platform and data products. Build SDKs, APIs, and reusable frameworks that make it easy for other engineering and research teams to access data and adopt the platform.

Ensure data quality and governance. Implement validation, reconciliation, and monitoring processes. Maintain data catalogs and metadata so teams can discover, trust, and reuse data assets across the organization.

Improve observability and operational health. Design monitoring, alerting, and logging for pipelines and infrastructure. You'll be expected to catch problems before users do.