Design resilient ingest pipelines from capture rigs to cloud storage, including recovery from partial uploads, unreliable networks, and hardware failures.
Build transcoding, normalization, synchronization, and quality-checking pipelines for multi-camera video, telemetry, and sensor data.
Create dataset versioning, lineage, indexing, packaging, integrity verification, and access-control systems for delivery to robotics laboratories.
Optimize storage tiering, lifecycle management, egress, encoding efficiency, GPU utilization, and cost per captured hour or delivered dataset.
Improve monitoring, logging, tracing, throughput, failure metrics, latency, reliability, and incident recovery across the pipeline.
Troubleshoot issues spanning media tooling, storage, networking, and application code, and participate in production on-call rotation.
Own complex initiatives from problem definition through design, implementation, operation, and performance measurement.
Participate in code reviews and provide practical technical feedback.
Requirements
5+ years of experience building and operating production video or media-processing systems at scale.
Production experience with FFmpeg, GStreamer, or an equivalent media framework, including building and debugging pipelines under load.
Fluency with H.264, HEVC, AV1, MP4, fMP4, and the tradeoffs among codecs and containers.
Strong understanding of distributed systems, cloud infrastructure, object storage, and observability.
Experience with durable workflow orchestration such as Temporal, Conductor, Step Functions, Airflow, or similar tools.
Experience designing cost-efficient architectures for large media or data workloads, including storage tiering, egress, capacity planning, and GPU utilization.
Experience with infrastructure as code, CI/CD, and production operations.
Strong programming and problem-solving skills.
Preferred: experience with multi-camera or time-synchronized capture systems and clock-drift or alignment problems.
Preferred: familiarity with MCAP, rosbag, ROS, ROS2, Parquet, or similar robotics and sensor-data formats.
Preferred: familiarity with machine learning on video or images, including classification, detection, tracking, segmentation, re-identification, super-resolution, quality scoring, auto-annotation, or redaction.
Preferred: experience with perceptual quality measurement, VMAF, encoding-ladder design, HLS, DASH, CMAF, DRM integration, or annotation and review tooling.
Benefits
Free snacks.
Health insurance and personal insurance.
Flexible hours.
Maternity and paternity leave.
Broadband reimbursement.
The company states that teams work in person five days a week, with hubs in San Francisco, Bangalore, and Chicago and offices in New York, Phoenix, and Singapore.
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