Senior Media Platform Engineer

About the role

Our Senior Media Platform Engineer role will build and operate the workflow orchestration and integration layer that powers FOX’s internal software-defined media services and AI-enabled workflows. This position focuses on developing the “glue” between platforms—workflow engines, microservices, APIs, data stores, and observability—so FOX can reliably deploy, scale, and support AI-assisted media capabilities across production and distribution environments. This role works closely with multiple engineering teams and operational stakeholders to translate user needs into repeatable, governed workflows (POC → pilot → production) using modern software engineering practices and cloud-native delivery patterns. You will help FOX standardize how workflows are defined, validated, released, and monitored—especially for high-value AI use cases such as transcription, dubbing, highlights, video/audio analysis, and generative media services.

Responsibilities

• Design and implement workflow orchestration for media and AI pipelines (job definitions, routing rules, scheduling constraints, approvals, retries, and error handling)
• Build and maintain platform services that standardize how workflows are executed and governed across environments (dev/test/prod)
• Develop reusable workflow templates, SDKs, and integration patterns that accelerate delivery and reduce one-off implementations
• Implement policy and guardrails for workload isolation (e.g., real-time vs batch vs experimental) in partnership with platform/SRE teams
• Partner with AI/ML stakeholders to evaluate model options (build vs buy vs fine-tune), including selection criteria such as quality, latency, cost, and operational complexity
• Support model implementation into production workflows, including packaging, versioning, deployment patterns, rollback strategies, and reproducibility
• Contribute to model lifecycle practices such as dataset/labeling requirements, evaluation/benchmarking metrics, A/B testing approaches, and periodic model refresh processes
• Help operationalize model governance: traceability of model versions, model performance monitoring in production, and release notes tied to workflow versions
• Implement workflow-level validation and acceptance checks (input validation, output verification, automated quality gates) to ensure reliable, repeatable outcomes
• Define “production-ready” criteria for workflows (telemetry, error handling, documentation, runbooks, support handoffs) and enforce them through automation
• Partner with operations and reliability teams to ensure workflows can be supported in a 24/7 environment, with clear escalation paths and on-call expectations where appropriate
• Build production-grade services with strong engineering discipline: code reviews, automated testing, secure coding practices