Company Description

NorBot is Canada’s first dedicated physical AI robotics hub, bringing advanced robotics technology to Canadian businesses from initial inquiry through full integration. The company partners with leading global robotics manufacturers to deliver a comprehensive stack of robotics services. NorBot provides sales, development, deployment, maintenance, and consultation, all under one roof and all within Canada. Team members collaborate closely with businesses across sectors to design and implement robotics solutions that improve efficiency and innovation.

Joining NorBot means contributing to the growth of cutting-edge robotics applications in the Canadian market.

The role

You'll own the fleet-management, telemetry, and data layer that underpins our RaaS model. You'll build the system that collects data from both our appliance-class service robots and our Physical AI platforms, turn it into the operational backbone that runs our service offering, and produce the category-level dataset that becomes our long-term moat. This is an end-to-end ownership role with real product judgment attached. You won't just build a pipeline to spec — you'll decide what to build first, what evidence to capture, and what not to build yet. We value the simplest thing that works, extended only as real usage justifies it. If you over-architect before there's evidence, this isn't the role for you.

What you'll own

The fleet-management system — a single operational view of every deployed robot (status, uptime, location, task completion) across both bot types. The telemetry pipeline — reliable collection, storage, and processing of robot data from the field into our systems. The commercial data layer — the SLA tracking, service scheduling, and evidence base that our insurance and leasing models depend on. The category-level dataset — task type, environment, and deployment outcomes that form our durable data asset. The data firewall — keeping moat-relevant category data cleanly separated from customer operational data, sensor data, and IP. Reporting and dashboards — turning fleet data into decisions for us, and for customers where appropriate.

The product side of the role

You'll be making product calls, not just engineering ones. Norbot's leadership sets the high-level roadmap; you own how the data layer gets there: Sequencing what to build against where deployments actually are, rather than a speculative wishlist. Deciding what telemetry is worth capturing now versus later, and what each dashboard needs to answer for the person looking at it. Treating the SLA/insurance evidence base as a product surface — knowing who consumes it and what makes it credible. Spotting where the system should be modular so pieces can be swapped or rebuilt later, without over-planning for futures that may not arrive.

Why it's Important

Two bot types produce different telemetry. Appliance bots give clean operational data; Physical AI bots give richer, use-case-specific data. One layer has to serve both without flattening the difference. The insurance and leasing value depends on real fleet-hours of evidence. You build the apparatus that earns that evidence over time — it doesn't exist on day one. The data firewall is a privacy-and-architecture problem (PIPEDA / BC PIPA), not just an engineering one. Separation of moat data from customer and personal data has to be designed in, not bolted on.

You're a strong fit if you

Are a strong data / backend / platform engineer who's comfortable owning a system end-to-end: ingestion, storage, processing, and reporting. Bring product instincts — you build the simplest thing that works and extend it as evidence demands, and you can explain why you sequenced it that way. Are comfortable with messy, real-world device / IoT-style data from the field. Understand data-privacy and data-separation concerns and design with them in mind. Work cleanly alongside others' outputs — you'll consume the robot/systems stack and perception work and own everything downstream.

Bonus

Experience with IoT, telemetry, or fleet/device-management systems. Exposure to RaaS, hardware-enabled, or field-deployed software businesses. Familiarity with Canadian privacy regimes (PIPEDA / BC PIPA).