SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Instawork Robotics Labs (IRL) is building infrastructure to close the "100,000-year data gap" between AI language models and what physical robots need to learn. The company deploys skilled workers into real commercial and residential environments to capture high-fidelity task data for robotics foundation models.
As a Video Platform Engineer (E3), you will own the end-to-end pipeline that transforms raw multi-camera video capture into training-ready datasets. This hybrid media-engineering and distributed-systems role spans ingest, transcoding, time-synchronization, automated quality checks, indexing, and delivery to robotics labs.
Key responsibilities include:
- Design and build resilient ingest paths from capture rigs to cloud storage, handling partial uploads, flaky field networks, and mid-session hardware failures
- Build transcoding and normalization pipelines that convert heterogeneous rig output into consistent, analysis-ready media
- Time-synchronize multi-camera video with telemetry and sensor streams into coherent, labeled episodes
- Implement automated quality checks to flag unusable sessions (blur, exposure failure, dropped frames) before delivery
- Design dataset versioning, lineage, and indexing for full traceability from shipped episodes back to raw capture
- Build delivery paths for datasets to robotics labs, including packaging, integrity verification, and access control
- Own cost per captured hour and cost per delivered dataset through storage tiering, lifecycle management, egress optimization, encoding efficiency, and GPU utilization
- Improve observability across the pipeline with monitoring, logging, tracing, and per-stage metrics
- Troubleshoot complex issues spanning media tooling, storage, networking, and application code
- Participate in on-call rotation and own diagnosis and resolution of production incidents
- Participate in code reviews and raise technical standards
- Own complex initiatives from problem definition through design, implementation, and operation
- Measure and improve pipeline throughput, end-to-end latency, dataset yield, reliability, incident recovery time, and infrastructure cost
You will work directly with data collection teams in the field and have real input into which problems are solved first.
REQUIREMENTS:
- 5+ years building and operating production video or media processing systems at scale
- Hands-on production experience with FFmpeg, GStreamer, or equivalent media framework; you've built pipelines around them and debugged under load
- Fluency in codecs and containers: H.264/HEVC/AV1, MP4/fMP4, and practical tradeoffs 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, with working understanding of storage tiering, egress, capacity planning, and GPU utilization
- Experience with Infrastructure as Code, CI/CD, and production operations
PREFERRED:
- Multi-camera, multi-view, or time-synchronized capture systems and associated clock-drift and alignment problems
- 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, 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
- Full-stack range; experience building annotation or review tooling on top of your own pipeline
