Software Engineer, Broadcast & Video Formats

Luminegrp; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Luminegrp is seeking an engineer with deep broadcast and video knowledge to transform that expertise into reliable software within our MAM platform. You'll own media handling features, codecs, and format standards across ingest, analysis, transcoding, and delivery in containerized workflows on Kubernetes.

You will collaborate with senior architects, contribute to CI/CD, and help ensure accuracy of metadata and interoperability with customer specifications.

Qualifications

• Hands-on experience in broadcast, post-production, or MAM environments.
• Strong knowledge of common video codecs and container formats (e.g., H.264/AVC, HEVC, ProRes, MXF).
• Experience with media metadata and subtitle formats.
• Practical experience with FFmpeg and media-analysis tools.

Responsibilities

• Implement and maintain media-handling features within the MAM platform.
• Integrate and tune transcoders and media tools in containerized workflows.
• Map and validate metadata against broadcast standards and customer specs.
• Contribute to CI/CD pipelines and code quality with SonarQube/Mend.

Skills

Broadcast experience

Video formats & codecs

REST APIs

English communication

Tools

FFmpeg

MediaInfo

Kubernetes

Helm

GitOps workflows

Azure DevOps

GitHub

Job description

Описание:

We are looking for an engineer who knows the broadcast and video world in depth and can turn that knowledge into reliable software. You will be our go-to person for video formats, codecs, and broadcast-specific media handling within our MAM platform. You will work in a team on Kubernetes-based, GitOps-delivered services, with architectural guidance from our senior architects.

Задачи:

Implement and maintain media-handling features: ingest, analysis, transcoding, proxy generation, and delivery in broadcast-grade formats. Analyze and resolve format-related issues (non-conformant files, interlace/frame-rate problems, audio channel mapping, timecode and metadata errors). Integrate and tune transcoders and media tools (e.g. FFmpeg, MediaInfo, commercial transcoding engines) in containerized workflows. Map and validate technical and descriptive metadata against broadcast standards and customer specifications. Support customer projects and escalations with media-domain expertise. Contribute to code quality and security (SonarQube, Mend) and to CI/CD pipelines. Document format and workflow knowledge so it spreads across the team.

Требования:

Hands-on experience in broadcast, post-production, or MAM environments. Solid knowledge of the video formatsin everyday use: Codecs: H.264/AVC, HEVC, MPEG-2, ProRes, DNxHD/DNxHR, XDCAM HD/IMX, AVC-Intra, JPEG 2000 Containers: MXF (OP1a, OPAtom), MP4/MOV, MPEG-TS, ideally IMF Video fundamentals: interlaced vs. progressive, frame rates (incl. 29.97/59.94 and drop-frame timecode), aspect ratios, color spaces (BT.601/709/2020), basics of HDR (PQ/HLG) Audio: channel layouts and mapping, sample rates, loudness (EBU R128) Experience with media metadata and subtitle/caption formats (e.g. EBU Core, SMPTE, BWF, EBU-TT/STL, TTML, CEA-608/708). Practical experience with FFmpeg and media analysis tooling. Programming experience in [Java / .Net / C#] and comfort working with REST APIs. Working knowledge of Kubernetes and Helm, plus Git-based workflows. Structured, quality-conscious way of working; fluent English, German is a plus. Tooling and stack Platform: Kubernetes, Helm, GitOps (Flux or Argo CD) Cloud: AWS (working knowledge: S3, EKS, IAM basics) CI/CD: Azure DevOps, GitHub Quality & security: SonarQube, Mend (day-to-day use, fixing findings) Media tooling: FFmpeg, MediaInfo, MXF analysis tools Nice to know: OpenTelemetry/Grafana for debugging media pipelines Experience with Vidispine or similar MAM platforms (Avid, Dalet, Telestream, etc.) Delivery specs such as AS-11 (UK DPP), IMF, or broadcaster-specific specifications (e.g. ARD/ZDF) Knowledge of live and IP workflows (SMPTE ST 2110, SRT) Experience with AI/ML-based media enrichment (speech-to-text, face/object detection) Familiarity with AWS media services (MediaConvert, etc.) Curious and detail-oriented, enjoys getting to the root cause of a broken file Explains media topics clearly to engineers who are not broadcast experts Reliable team player who takes ownership of their area and asks early when blocked