ABOUT THE ROLEOur 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 14 pilot 14 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.
ABOUT YOU- You are excited by new technology and its potential to change how things are done
- You LOVE getting stuff done, not just talking about it!
- You take ownership in your products and their success in the company
- You enjoy building systems that other teams depend on, and you care about reliability and maintainability
- You can translate ambiguous user needs into clear software requirements, APIs, and deliverable milestones
- You value automation, testing, and observability as first-class parts of engineering
- You like working across teams to evaluate options and deliver the best solution-not just "a" solution
A SNAPSHOT OF YOUR RESPONSIBILITIESWorkflow Orchestration & Platform Services
- 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
AI Model Selection, Training & Lifecycle Enablement
- 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
Quality Gates, Validation & Operational Readiness
- 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
Software Development, CI/CD & Observability
- Build production-grade services with strong engineering discipline: code reviews, automated testing, secure coding practices, and documentation
- Implement and improve CI/CD pipelines for workflow services (build, test, deploy, promote, rollback) in partnership with platform teams
- Ensure services are observable and supportable (structured logs, metrics, tracing, dashboards, and operational runbooks)
- Perform root cause analysis and automate repetitive operational tasks to reduce toil and improve reliability
Collaboration & Client Outcomes (Internal + External)
- Work across engineering teams and stakeholders (Broadcast, Video, Monitoring & Control, Networking, Media Infrastructure) to define requirements, evaluate alternatives, and deliver the best solution for FOX workflows
- Translate user and operator requirements into clear user stories and acceptance criteria ("As a user, I need...")
- Support internal clients (MCR/operations, production engineering, distribution teams) and, when applicable, improve FOX's ability to support external partners through better reliability, visibility, automation, and repeatable delivery patterns
WHAT YOU WILL NEED- 5+ years professional software engineering experience building and operating production services (APIs, microservices, distributed systems)
- Strong experience with cloud-native development practices: containers, service orchestration concepts, and CI/CD delivery
- Solid programming ability in one or more common backend languages (e.g., Python, Go, Java, TypeScript) and comfort building RESTful APIs and integrations
- Experience integrating with data systems (SQL/NoSQL), message/event systems, and modern authentication/authorization patterns
- Experience evaluating and integrating practical AI/ML capabilities into production systems, including automation, intelligent monitoring, workflow optimization, or developer tooling
- Strong troubleshooting mindset: ability to debug production issues and implement durable fixes
- Strong communication skills and comfort working cross-functionally with technical and non-technical stakeholders
NICE TO HAVE, BUT NOT A DEAL BREAKER- Experience with workflow orchestration tools or event-driven architectures
- Familiarity with observability tooling and SRE-style operational practices (SLIs/SLOs, alerting, postmortems)
- Understanding of modern AI technologies such as LLM APIs, vector databases, and inference services, with the ability to apply them pragmatically in scalable, reliable environments
- Experience integrating AI/ML inference services, MLOps patterns, or contributing to model evaluation/fine-tuning workflows
- Familiarity with Kubernetes/OpenShift operations and infrastructure-as-code concepts
- Exposure to live media/broadcast concepts (helpful, but not required-we can teach)
- Bachelor's Degree or equivalent practical experience in Engineering, IT, Computer Science or related technical field
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Pursuant to state and local pay disclosure requirements, the pay rate/range for this role, with final offer amount dependent on education, skills, experience, and location is $143,000.00-173,000.00 annually. This role is also eligible for various benefits, including medical/dental/vision, insurance, a 401(k) plan, paid time off, and other benefits in accordance with applicable plan documents. Benefits for Union represented employees will be in accordance with the applicable collective bargaining agreement.
View more detail about FOX Benefits.
