Machine learning systems engineer
Pubblicato il 08-08-2026 - Tether.io in Italia
About The Job We are developing a highly scalable media intelligence platform that processes, analyzes, and structures large volumes of multimedia content across text, image, video, and audio. As a Senior Applied ML Engineer , you will architect and build the core backend systems that power media ingestion, processing workflows, metadata generation, AI‐based analysis, semantic search, and retrieval across large media libraries. We are looking for a senior applied ML engineer who can design, implement, optimize, and evaluate a production‐grade moderation pipeline using open‐source models. This role requires deep backend engineering expertise, strong system design capability, and practical experience integrating AI/ML systems into production workflows. You will work on complex media‐processing pipelines, video/audio analysis, OCR, speech‐to‐text, embedding generation, vector search, multimodal model integrations, and high‐throughput asynchronous workloads. You will collaborate closely with engineering leadership to define backend architecture, improve reliability and scalability,
and guide other engineers in delivering secure, observable, and high‐performance systems. Responsibilities Architect, build, and operate scalable backend services for a media intelligence platform, with a focus on clean, maintainable, and production‐ready systems. Own critical backend components end to end, from system design and API contracts through implementation, deployment, monitoring, and iteration. Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model‐serving workflows. Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails. Design high‐throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content. Build distributed, event‐driven workflows for m