The RoleWe are hiring a
Senior Backend Engineer to build the systems that ingest, process, store, and serve
petabyte-scale video and sensor data.
This is a true senior role. You will own architecture and reliability for production services that operate at extreme scale:
heavy cloud networking,
distributed GPU compute (thousands of GPUs),
millions of GPU-hours, and massive video pipelines.
Ideal background: backend systems for video services (e.g., YouTube, Twitch, Netflix, TikTok, video ads/streaming, CDN/edge video, large-scale media processing).
What You'll Work OnVideo + Data Platform Backend- Design and build high-throughput services for upload/ingest, processing, indexing, retrieval, and access control.
- Build APIs and internal primitives that make large datasets usable for research, labeling, QA, and model training.
Scale, Reliability, and Quality- Own service quality end-to-end: correctness, latency, uptime, capacity planning, and cost.
- Build observability (metrics/logs/tracing), incident response, and operational tooling that prevents recurring failures.
Distributed Compute + Networking- Build systems that coordinate large jobs across cloud infrastructure and GPU fleets.
- Make data movement reliable and fast: storage I/O, network throughput, queuing, retries, idempotency, and backpressure.
Who You AreRequired Background- 7+ years of backend/software engineering experience (or equivalent impact).
- Strong system design fundamentals and proven experience operating production systems.
- Excellent debugging and operational judgment (you can find the root cause, not just patch symptoms).
Strong Signals- You have built backend systems for video/media at scale (streaming, transcoding, processing, storage, CDN/edge, ingestion, metadata/search).
- You think in terms of failure modes, invariants, and clean interfaces.
- You write maintainable systems with crisp docs and strong ownership.
Why This Role- Build the backend that powers petabyte-scale robotics datasets and the training pipelines that depend on them.
- Operate at a scale that forces great engineering: data volume, networking, reliability, and compute orchestration.
- High ownership and direct impact across product, operations, and research.
