Staff ML Systems Engineer - Media Intelligence

About the Job

We are building a highly scalable media intelligence platform that processes, analyzes, and flags potentially problematic content across large volumes of video, audio, image, and text. The platform powers content safety workflows for Tether Data products, including Keet, and must operate reliably at scale across diverse languages, formats, and content types.

As a Staff ML Systems Engineer, you will own the core of this platform - from ingestion and async processing pipelines through AI/ML model integration, inference optimization, vector search, and structured report generation. This is not a research role and it is not a prompt-engineering role. It is a production engineering role where the models are one component of a larger system that you are responsible for making fast, reliable, cost-efficient, and maintainable.

You will be the senior technical owner of the media intelligence backend. That means you define the architecture, make the hard tradeoff calls, mentor other engineers, and carry responsibility for the system in production. You will work closely with engineering leadership and collaborate with ML researchers, data engineers, and product teams to deliver a platform that provides actionable, timestamped findings to human reviewers at scale.

Responsibilities

• Backend Architecture & System Ownership
• Design and operate scalable backend services for media ingestion, processing, and report generation - clean, well-tested, and built for horizontal scaling from day one
• Own API contracts, data models, and storage patterns for media assets, processing jobs, model outputs, embeddings, and audit trails
• Build high-throughput async processing pipelines for video, audio, image, and text using queues and event-driven patterns (SQS, Kafka, Pub/Sub)