Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The role
Every hour our operators work produces hours of synchronized video, robot state, and actions, and that data is a core product. You'll own the path from robot site to a searchable, trustworthy dataset that our researchers train on and our customers buy. You'll also run the infrastructure that keeps a growing, geographically spread fleet updated, monitored, and healthy.
What you'll do
Own data storage and lifecycle: ingest from sites, object storage layout, retention, tiering, and cost.
Build the catalog: episode metadata, task labels, operator and robot info, quality flags, and search, so anyone can find "all successful pick-and-place episodes on arm model X last month."
Build automated quality checks: dropped frames, sync drift, missing streams, failed or truncated episodes.
Build dataset export and delivery: convert raw episodes into the formats training teams and customers expect, with versioning and provenance.
Handle data governance: access control, customer and site data agreements, privacy (faces, sensitive scenes on site), and audit trails for data we sell.
Run fleet infrastructure: provisioning edge machines at sites, remote software deploys and rollbacks, secrets, VPN/connectivity, monitoring, and alerting.
Keep cloud costs and bandwidth under control as data volume grows.
Build dashboards to monitor data collection status and progress
What we're looking for
3-4 years as a data, platform, or infrastructure engineer owning production systems end to end.
Experience with large unstructured data (video, sensor logs, images) in object storage, not only tabular warehouses.
Strong Python and SQL; comfortable with infrastructure-as-code, containers, and CI/CD.
You've built pipelines with real failure handling: retries, idempotency, backfills, and monitoring.
Nice to have
Experience with robotics or autonomous-vehicle data, or ML training data pipelines.
Edge fleet management: deploying to many remote Linux devices on unreliable networks.
Experience packaging and delivering datasets to external customers.
Familiarity with data privacy rules across multiple countries.
If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.
We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.
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