Video Platform Engineer

Responsibilities:

• Design and build the ingest path from capture rigs to cloud storage, resilient to partial uploads, flaky field networks, and mid-session hardware failure.
• Build transcoding and normalisation pipelines that turn heterogeneous rig output into consistent, analysis-ready media.
• Time-synchronise multi-camera video with telemetry and sensor streams into coherent, labelled episodes.
• Build automated quality checks that flag unusable sessions before they reach a customer - blur, exposure failure, dropped frames, etc.
• Design dataset versioning, lineage, and indexing so that any shipped episode can be traced back to its raw capture.
• Build the delivery path that gets datasets to robotics labs, including packaging, integrity verification, and access control.
• Own cost per captured hour and cost per delivered dataset: storage tiering and lifecycle, egress, encoding efficiency, and GPU utilisation.
• Improve observability across the pipeline—monitoring, logging, tracing, and per-stage throughput and failure metrics.
• Troubleshoot complex issues spanning media tooling, storage, networking, and application code.
• Participate in an on-call rotation and own diagnosis and resolution of production issues.
• Participate in code reviews and raise the technical bar through clear, practical feedback.
• Own complex initiatives from problem definition through design, implementation, and operation.
• Measure and improve pipeline throughput, end-to-end latency from capture to delivery, dataset yield, reliability, incident recovery time, and infrastructure cost per workload.

Requirements:

• 5+ years building and operating production video or media processing systems at scale.
• Hands-on, production experience with FFmpeg, GStreamer, or an equivalent media framework.
• You've built a pipeline around it and debugged it under load.
• Fluency in codecs and containers: H. 264/HEVC/AV1, MP4/fMP4, and the practical trade-offs between them.
• Strong understanding of distributed systems, cloud infrastructure, object storage, and observability.
• Strong programming and problem-solving skills.
• Experience with durable workflow orchestration (Temporal, Conductor, Step Functions, Airflow, or similar).
• Experience designing cost-efficient architectures for large media or data workloads, and a working understanding of storage tiering, egress, capacity planning, and GPU utilisation.
• Experience with Infrastructure as Code, CI/CD, and production operations.

Preferred:

• Multi-camera, multi-view, or time-synchronised capture systems, and the clock-drift and alignment problems that come with them.
• Robotics or sensor data formats: MCAP, rosbag, ROS/ROS2, Parquet, or similar columnar and log formats.
• Familiarity with machine learning on video or images.
• Classification, detection and tracking, segmentation, re-identification, super-resolution, quality scoring, auto-annotation, redaction.
• Perceptual quality measurement (VMAF or equivalent) and encoding-ladder design.
• Streaming and packaging formats: HLS, DASH, CMAF, and DRM integration.
• Range across the stack. If you've built the annotation or review tooling on top of your own pipeline, we want to hear about it.