Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We partner with leading AI labs and robotics companies to deliver high-quality, real-world datasets used to train, evaluate, and deploy robotic systems — where model performance is dictated by data quality.
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 BackendDesign 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.
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.
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.
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.
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.
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