Lead Software Engineer, Device OS

Apkudo builds an intelligence layer for the device lifecycle, connecting industrial machines and applications that inspect, test, clean, sort, store, and retire devices. The Lead Software Engineer will build and own Device OS integration, software and model deployment, remote monitoring, and unified control systems for managing a distributed fleet at scale.

Responsibilities:

• Build the integration layer. The services and contracts that connect machines and applications in the field to the Apkudo platform — identity, configuration, command, and event flow in both directions
• Own the deployment mechanism. How Apkudo software reaches the ecosystem: application releases, computer vision and diagnostic models, and agents. Packaging, distribution, staged rollout, versioning, and rollback across a heterogeneous fleet
• Build remote monitoring. Telemetry, health, and diagnostics for every machine on the platform, and the alerting and investigation tooling built on top of it
• Deliver a unified control surface. A single place to see, configure, and operate machines and applications, rather than a different path for every machine type
• Partner with robotics engineers on interface design. You are the counterpart who answers “how should my machine talk to the platform,” and you are accountable for that answer being a good one. Developer experience is part of the job, not an afterthought
• Partner with design on the operator experience. Monitoring and configuration are an operator-facing product, not an internal console with a table in it
• Collaborate across the platform. Work with Device AI on getting models and agents from training into production on real hardware, and with Device Passport on the event schema everything in the field emits

Requirements:

• Significant experience building and shipping production backend or full-stack software, including technical leadership of a substantial system
• Strong distributed systems fundamentals: services, APIs, messaging, and the failure modes that come with an unreliable network between you and the thing you are managing
• Experience with deployment or release infrastructure — artifact distribution, versioning, staged rollout, rollback — and a clear point of view on what makes a deploy safe
• Experience with observability in practice: instrumentation, metrics, logs, alerting, and remote diagnosis of something you cannot physically touch
• You design interfaces as products. You have written an API or SDK that other engineers depended on, and improved it based on how they actually used it
• Comfort working across an organizational boundary with people who are not software engineers — hardware, robotics, operations — and translating between what they need and what the platform can offer
• Willingness to contribute to user-facing surfaces, or to partner closely with design to get them right
• Genuine engagement with where AI is going — using modern AI tooling in your own work, and interested in the problem of getting models and agents deployed and operating reliably in production
• Authorized to work in the United States
• Experience with fleet, edge, or IoT device management at scale — provisioning, configuration, over-the-air update, or mobile device management
• Experience with model deployment or MLOps, particularly getting models onto constrained or edge runtimes
• Experience building for a heterogeneous fleet you do not fully control, where each unit has a different configuration and cannot simply be replaced
• Platform or developer-tools work where external teams were the customer, including conformance or certification test suites
• Hands-on experience deploying agentic systems, or with agent tooling and protocols such as MCP
• Domain exposure to industrial automation, manufacturing systems, logistics, repair, or recommerce