What We Build
Physical AI breaks after deployment. Models drift, conditions change, and on the edge there is no cloud to catch it. Edgeble self-correcting runtime keeps deployed AI accurate on-device, without stopping inference. It is in production at Tier-1 manufacturers and hardware-agnostic across edge NPUs.
The Role
Work on the deployment side of self-correcting Physical AI in industrial and automotive environments. The job is to take the runtime from working software to field-ready systems that survive noisy sensors, changing conditions, customer constraints, and long-running operation. You will help shape deployment validation, field integration, and the practical feedback loop between system behavior and model correction.
How We Work
Edgeble is agent-native. We build with agentic coding workflows daily on internal platform tooling already set up for it. You direct the agents; your judgment goes on what agents cannot do: correction logic, validation design, and what better means. If you would rather type every line yourself, this role will frustrate you, self-select accordingly.
You
Expected stream: Diploma or B.Tech in ECE, Mechanical, or Computers. Useful background includes embedded systems, deployment engineering, industrial automation, automotive software, or applied ML in physical systems. We care about the ability to reason about real hardware, real field conditions, and repeatable deployment behavior.
Why Here
Own a defined layer of a production, patent-pending runtime at the moment it scales. Early-team equity, direct work with a founder with 19 years of experience across silicon, embedded systems, Edge AI, Linux kernel/U-Boot maintenance, and physical systems, in the working style most teams are still debating.
Deep-tech Work India