3-4 years as a data, platform, or infrastructure engineer managing production systems end to end.
Experience with large unstructured data types like video and sensor logs in object storage.
Proficient in Python and SQL; familiar with infrastructure-as-code, containers, and CI/CD practices.
Experience building robust data pipelines with failure handling mechanisms.
Responsibilities
Own data storage and lifecycle management, including ingestion and cost control.
Build a comprehensive catalog for episode metadata and searchability.
Develop automated quality checks for data integrity and completeness.
Create dataset export processes for training teams and customers, ensuring versioning and provenance.
Manage data governance, including access control and privacy compliance.
Oversee fleet infrastructure, including provisioning and remote software management.
Monitor cloud costs and bandwidth as data volume increases.
Benefits
Flexible work environment with opportunities for remote work.
Access to cutting-edge technology and tools in the robotics field.
Collaborative team culture that encourages innovation and learning.
Opportunities for professional growth and skill development.
Full Job Description
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 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.
