Top Benefits
About the role
About Monaro
Monaro is building the intelligence layer for mechanical teams. Our platform reads equipment schedules straight out of PDF construction drawings, runs the quantity takeoff, manages supplier pricing requests and produces a formatted quote. Work that used to take an estimator six hours now takes under twenty minutes.
Our customers are HVAC distributors and mechanical contractors. They win or lose commercial projects on how fast and how accurately they can quote, and most of them still do it by hand today. We want to own that moment end to end and become the system these teams run their business on. Monaro is a product within Torinit's venture studio. Torinit is VC-backed, which means you get early-stage product ownership with the engineering bench and funding of an established company behind it.
The Role
This is the role that shapes how Monaro gets built. You will lead engineering as a player-coach, reporting to the Founder, and work with lead engineers to develop the technical roadmap for the platform. The document extraction pipeline is the product. It has to read construction drawings accurately enough that an estimator will stake a commercial bid on what comes out.
Roughly 30% of your time is hands-on, most of it on that pipeline and the architecture around it. The other 70% goes to building the engineering team and improving the way we ship software.
You will be part of our Canadian team, working through senior engineers and leads in our India office, part of a 20+ person engineering group. This is one engineering organization rather than a head office and an outsourced arm. You are the first engineering leader dedicated to Monaro, so the standards and the technical direction are yours to set, but you are setting them with an experienced team already in place rather than from nothing. Expect some early-morning overlap with the India team as part of the rhythm. You will also work closely with Product, Design and our go-to-market team.
Responsibilities
Own the AI core of the product. The extraction pipeline that turns unstructured construction drawings into structured equipment schedules, how we measure its accuracy, and how we keep improving it against real customer documents. Own how we evaluate model output. Accuracy here is not a research metric, it is what a customer bets a bid on. You define the evaluation approach, the thresholds, and what happens when the model is unsure. Own the architecture of the rest of the platform, including the multi-tenant application layer, integrations with customer ERP systems, and the decisions that let all of it scale as we grow. Make growth cheap. As we add customers, the cost of onboarding one, the cost of running an extraction, and the engineering effort per new account all need to trend down rather than up. Set the quality bar for how we build. Security, performance and reliability expectations that hold across the product. Our customers are enterprises handling commercially sensitive bid data, and our security posture is part of why they choose us. Standardize our delivery platform. CI/CD pipelines, container practices, environment management and infrastructure-as-code, so shipping is fast and boring. Own technical hiring. You run the interview loop and have final sign-off on engineering hires. We want engineers who understand the customer's workflow as well as the code. You define that profile and hire against it. Set how we use AI in our own engineering, covering coding tools, agentic workflows and guardrails. We expect this to change how the team works and you decide how. Build the habits of a strong engineering culture. Architecture reviews, decision records, research before building, and mentoring that grows engineers into technical leaders. Partner with Product on what is feasible, what it costs and what we sequence next. You will also sit in on enterprise sales conversations when the customer wants technical depth in the room.
Required Skills & Experience
10+ years in software engineering, including production ownership of a SaaS product at scale. You have been accountable for its performance, security and observability. Applied AI/ML you have actually shipped rather than piloted. Document understanding, OCR or computer vision, extraction pipelines, or comparable model-driven systems, with a real method for judging output quality. You can walk us through what you built, what it got wrong, and how you knew. You have built and operated multi-tenant systems where reliability and data isolation were commitments customers could hold you to. You have introduced engineering standards across a growing team and made them stick without slowing delivery. Track record growing senior engineers and running technical hiring. You work well close to customers and the business. You can turn a workflow problem into an architecture and defend the tradeoffs to a non-technical audience. Experience leading a distributed team across time zones, including working through senior engineers and leads rather than only managing directly.
Preferred
Depth in TypeScript/Node and React, with working Python. That is our primary stack. AWS, Docker/Kubernetes, GitHub Actions or equivalent, and infrastructure-as-code. Ideally you have built delivery standards and not only worked within them.
Active use of AI coding tools and agentic workflows in your own work
Cost and performance engineering for AI workloads, including inference spend, caching, and the tradeoffs that decide whether a feature is viable at customer volume Data engineering behind a product: pipeline design, warehouse modelling, and the analytics layer customers see Enterprise integration experience, particularly ERP or other systems of record You have taken a product through enterprise security review, SOC 2 or comparable audit expectations without stalling the roadmap Early-stage product experience, taking something from first customers to a scaling platform
What Success Looks Like in Year One
We expect the customer base to grow significantly over your first year, and most of what makes this role hard follows from that.
We have an evaluation approach we can defend to a customer, with thresholds, a clear behaviour when the model is unsure, and regression testing that runs before a model change ships rather than after a customer finds it. Extraction accuracy has measurably improved against real customer drawings, and improved fastest on the documents that were breaking us. Cost per quote is falling while volume rises. You know the unit economics of an extraction and you are engineering against them. The platform holds up as a system customers run their business on. Multi-tenant isolation, throughput under concurrent load, and reliability that survives being someone's critical path during bid season. We can pass an enterprise security review without it derailing a quarter. Bigger customers bring questionnaires, isolation requirements and audit expectations, and we are ready for them instead of reacting. ERP integration is a repeatable pattern rather than a bespoke project each time. The team has grown and the leads in India are operating with real autonomy. You have hired against a profile you defined, and delivery does not bottleneck on you.
Why Monaro
The central AI problem here is not solved. Reading unstructured construction drawings reliably, at an accuracy an enterprise estimator will stake a bid on, is genuinely hard, and it is the difference between a tool people try once and the system a business runs on.
The model is the product rather than a feature attached to one, which means most of the real work is the part teams tend to skip. Evaluation, edge cases, and the drawings that break everything. If that problem interests you there is a lot of it here, with paying customers already behind you.
What We Offer
Health Insurance: 100% employer-paid Time Off: 3 weeks vacation, statutory holidays, and 5 paid sick days Professional Development: Annual budget for certifications, courses and conferences Work Arrangement: Hybrid, 3 days a week in the Toronto office and 2 days remote Scope: First engineering leader dedicated to Monaro, working with an established 20+ person engineering group. The architecture and the standards are yours to define, on a platform that already has paying customers on it.
Backing: Early-stage product ownership inside Torinit's VC-backed venture studio
Growth Path: Grow into the senior technical leadership seat as the engineering org builds out beneath you The Work: An AI product where the model is the product. Document understanding at an accuracy an estimator will stake a commercial bid on, which is hard, unsolved, and central to whether the business works.
Accommodation and Equal Opportunity
Monaro is an equal opportunity employer. We welcome applications from people of all backgrounds and we hire on merit and fit for the role. We provide accommodation for applicants with disabilities at every stage of our recruitment process. If you need accommodation at any point, let us know and we will arrange it with you.
Not the right fit? Search for Software Engineering jobs in Toronto, Ontario, Canada
About Torinit
Human + AI Workflow Transformation for Distributors.
Similar Jobs
Top Benefits
About the role
About Monaro
Monaro is building the intelligence layer for mechanical teams. Our platform reads equipment schedules straight out of PDF construction drawings, runs the quantity takeoff, manages supplier pricing requests and produces a formatted quote. Work that used to take an estimator six hours now takes under twenty minutes.
Our customers are HVAC distributors and mechanical contractors. They win or lose commercial projects on how fast and how accurately they can quote, and most of them still do it by hand today. We want to own that moment end to end and become the system these teams run their business on. Monaro is a product within Torinit's venture studio. Torinit is VC-backed, which means you get early-stage product ownership with the engineering bench and funding of an established company behind it.
The Role
This is the role that shapes how Monaro gets built. You will lead engineering as a player-coach, reporting to the Founder, and work with lead engineers to develop the technical roadmap for the platform. The document extraction pipeline is the product. It has to read construction drawings accurately enough that an estimator will stake a commercial bid on what comes out.
Roughly 30% of your time is hands-on, most of it on that pipeline and the architecture around it. The other 70% goes to building the engineering team and improving the way we ship software.
You will be part of our Canadian team, working through senior engineers and leads in our India office, part of a 20+ person engineering group. This is one engineering organization rather than a head office and an outsourced arm. You are the first engineering leader dedicated to Monaro, so the standards and the technical direction are yours to set, but you are setting them with an experienced team already in place rather than from nothing. Expect some early-morning overlap with the India team as part of the rhythm. You will also work closely with Product, Design and our go-to-market team.
Responsibilities
Own the AI core of the product. The extraction pipeline that turns unstructured construction drawings into structured equipment schedules, how we measure its accuracy, and how we keep improving it against real customer documents. Own how we evaluate model output. Accuracy here is not a research metric, it is what a customer bets a bid on. You define the evaluation approach, the thresholds, and what happens when the model is unsure. Own the architecture of the rest of the platform, including the multi-tenant application layer, integrations with customer ERP systems, and the decisions that let all of it scale as we grow. Make growth cheap. As we add customers, the cost of onboarding one, the cost of running an extraction, and the engineering effort per new account all need to trend down rather than up. Set the quality bar for how we build. Security, performance and reliability expectations that hold across the product. Our customers are enterprises handling commercially sensitive bid data, and our security posture is part of why they choose us. Standardize our delivery platform. CI/CD pipelines, container practices, environment management and infrastructure-as-code, so shipping is fast and boring. Own technical hiring. You run the interview loop and have final sign-off on engineering hires. We want engineers who understand the customer's workflow as well as the code. You define that profile and hire against it. Set how we use AI in our own engineering, covering coding tools, agentic workflows and guardrails. We expect this to change how the team works and you decide how. Build the habits of a strong engineering culture. Architecture reviews, decision records, research before building, and mentoring that grows engineers into technical leaders. Partner with Product on what is feasible, what it costs and what we sequence next. You will also sit in on enterprise sales conversations when the customer wants technical depth in the room.
Required Skills & Experience
10+ years in software engineering, including production ownership of a SaaS product at scale. You have been accountable for its performance, security and observability. Applied AI/ML you have actually shipped rather than piloted. Document understanding, OCR or computer vision, extraction pipelines, or comparable model-driven systems, with a real method for judging output quality. You can walk us through what you built, what it got wrong, and how you knew. You have built and operated multi-tenant systems where reliability and data isolation were commitments customers could hold you to. You have introduced engineering standards across a growing team and made them stick without slowing delivery. Track record growing senior engineers and running technical hiring. You work well close to customers and the business. You can turn a workflow problem into an architecture and defend the tradeoffs to a non-technical audience. Experience leading a distributed team across time zones, including working through senior engineers and leads rather than only managing directly.
Preferred
Depth in TypeScript/Node and React, with working Python. That is our primary stack. AWS, Docker/Kubernetes, GitHub Actions or equivalent, and infrastructure-as-code. Ideally you have built delivery standards and not only worked within them.
Active use of AI coding tools and agentic workflows in your own work
Cost and performance engineering for AI workloads, including inference spend, caching, and the tradeoffs that decide whether a feature is viable at customer volume Data engineering behind a product: pipeline design, warehouse modelling, and the analytics layer customers see Enterprise integration experience, particularly ERP or other systems of record You have taken a product through enterprise security review, SOC 2 or comparable audit expectations without stalling the roadmap Early-stage product experience, taking something from first customers to a scaling platform
What Success Looks Like in Year One
We expect the customer base to grow significantly over your first year, and most of what makes this role hard follows from that.
We have an evaluation approach we can defend to a customer, with thresholds, a clear behaviour when the model is unsure, and regression testing that runs before a model change ships rather than after a customer finds it. Extraction accuracy has measurably improved against real customer drawings, and improved fastest on the documents that were breaking us. Cost per quote is falling while volume rises. You know the unit economics of an extraction and you are engineering against them. The platform holds up as a system customers run their business on. Multi-tenant isolation, throughput under concurrent load, and reliability that survives being someone's critical path during bid season. We can pass an enterprise security review without it derailing a quarter. Bigger customers bring questionnaires, isolation requirements and audit expectations, and we are ready for them instead of reacting. ERP integration is a repeatable pattern rather than a bespoke project each time. The team has grown and the leads in India are operating with real autonomy. You have hired against a profile you defined, and delivery does not bottleneck on you.
Why Monaro
The central AI problem here is not solved. Reading unstructured construction drawings reliably, at an accuracy an enterprise estimator will stake a bid on, is genuinely hard, and it is the difference between a tool people try once and the system a business runs on.
The model is the product rather than a feature attached to one, which means most of the real work is the part teams tend to skip. Evaluation, edge cases, and the drawings that break everything. If that problem interests you there is a lot of it here, with paying customers already behind you.
What We Offer
Health Insurance: 100% employer-paid Time Off: 3 weeks vacation, statutory holidays, and 5 paid sick days Professional Development: Annual budget for certifications, courses and conferences Work Arrangement: Hybrid, 3 days a week in the Toronto office and 2 days remote Scope: First engineering leader dedicated to Monaro, working with an established 20+ person engineering group. The architecture and the standards are yours to define, on a platform that already has paying customers on it.
Backing: Early-stage product ownership inside Torinit's VC-backed venture studio
Growth Path: Grow into the senior technical leadership seat as the engineering org builds out beneath you The Work: An AI product where the model is the product. Document understanding at an accuracy an estimator will stake a commercial bid on, which is hard, unsolved, and central to whether the business works.
Accommodation and Equal Opportunity
Monaro is an equal opportunity employer. We welcome applications from people of all backgrounds and we hire on merit and fit for the role. We provide accommodation for applicants with disabilities at every stage of our recruitment process. If you need accommodation at any point, let us know and we will arrange it with you.
Not the right fit? Search for Software Engineering jobs in Toronto, Ontario, Canada
About Torinit
Human + AI Workflow Transformation for Distributors.