A very senior individual contributor to own the hardest backend architecture calls for a platform that must handle far more data than today. The work centers on distributed and data intensive systems, databases, processing systems, and APIs that customers use directly, plus raising engineering quality across teams through reviews and mentorship. A strong candidate has long hands-on experience designing, running, and re-architecting large production systems and has lived with the consequences of those trade-offs.
How it fits the AI era
The product is infrastructure for teams training physical AI systems, so the backend must stay reliable and fast as data volumes grow by orders of magnitude. AI tools can speed up routine coding and review, but the core judgment about how distributed systems fail under load and which trade-offs hold for years stays with the engineer. The role is about building the dependable base that AI work depends on, not about building models.
How it builds AI experience
Working here means gaining exposure to how AI teams manage, label, and clean video, sensor, and image data for robots and autonomous vehicles, which shapes real system design decisions. A candidate can build this by studying data pipelines and storage patterns for large sensor datasets and by practicing rigorous reasoning about failure and load. The posting is technology agnostic, so openness to learning the stack matters more than prior AI tooling.
Worth showing
- Production reliability and operations
- Data migrations and re-architecture
- Architecture and code review leadership
- Influence without authority
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Encord
A data platform that helps AI teams manage, label and clean the video, sensor and image data that trains robots, self-driving cars and other physical AI systems.
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