Learning Engineer
Zinata Inc.
About the role
Company Description
Chirone is an early-stage Toronto-based startup building what we believe will be the world’s first full-cycle learning aid — rooted in science, and initially focused on manufacturing. Our AI platform puts an expert and a coach in every worker’s pocket: capturing what your best people know, building it into personalized development plans, and delivering it in the moment — on the floor, at the machine, when it matters.
We’re at the beginning of something significant. If you want to be part of building it from the ground up, read on.
Role Description
We need someone who takes learning science seriously — not as a background reference, but as a design discipline.
As Learning Engineer, you’ll model learning behaviours, design the logic that drives our development plans and daily engagement, and help us apply the best available theory to a real industrial deployment environment. This is rare: most learning researchers work with small samples and controlled conditions. You’d be working with manufacturing workers at scale, with real operational data coming back.
What You'll Do
- Model how learning behaviours progress across skill levels, job contexts, and individual profiles
- Design and refine the logic behind personalized development plans and daily engagement sequences
- Apply spaced retrieval, habit formation, and situated learning principles to Chirone’s architecture
- Help operationalize the SECI knowledge spiral — particularly Externalization and Internalization — in an AI-mediated industrial context
- Contribute to research relationships with academic partners (including JAIST, Stanford, UCLA, and Washington University)
- Translate learning theory into product requirements the engineering team can build
What You Bring
- Deep fluency in the Toyota Production System as a learning architecture — standard work, kaizen, Genchi Genbutsu, the PDCA cycle — not just as operational tools but as expressions of how people build capability
- Familiarity with the core learning science traditions: Nonaka & Takeuchi’s SECI model, Bjork’s desirable difficulties, Roediger’s retrieval practice, Fogg’s Behavior Model, Wenger‑Trayner’s communities of practice
- Experience modelling learning behaviours — formally or through applied design work
- Comfort working at the intersection of theory and product: you can move from a research finding to a design decision
- Manufacturing or industrial context is a genuine asset; this work lives on the plant floor
Culture
Chirone is a small, focused team at an early and exciting stage. You will work directly with the founders, with real influence over product direction and the opportunity to see your thinking shape what gets built. We take the science seriously — intellectual rigour is genuinely valued here — and we balance it with the pragmatism of building a real product for real workers. If you are energized by meaningful problems and want your work to matter from day one, Chirone is a good fit.
Requirements
- Deep fluency in the Toyota Production System as a learning architecture — standard work, kaizen, Genchi Genbutsu, the PDCA cycle — not just as operational tools but as expressions of how people build capability
- Familiarity with the core learning science traditions: Nonaka & Takeuchi’s SECI model, Bjork’s desirable difficulties, Roediger’s retrieval practice, Fogg’s Behavior Model, Wenger-Trayner’s communities of practice
- Experience modelling learning behaviours — formally or through applied design work
- Comfort working at the intersection of theory and product: you can move from a research finding to a design decision
- Manufacturing or industrial context is a genuine asset; this work lives on the plant floor
Responsibilities
- Model how learning behaviours progress across skill levels, job contexts, and individual profiles
- Design and refine the logic behind personalized development plans and daily engagement sequences
- Apply spaced retrieval, habit formation, and situated learning principles to Chirone’s architecture
- Help operationalize the SECI knowledge spiral — particularly Externalization and Internalization — in an AI-mediated industrial context
- Contribute to research relationships with academic partners (including JAIST, Stanford, UCLA, and Washington University)
- Translate learning theory into product requirements the engineering team can build
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