Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Platform Engineer, Quality Platform
- Remote from
- Canada
- Salary
- CAD 107k–165k / yr
- Department
- QA & Testing
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 10 Dec 2026
- Listing views
- 16
- Application actions
- 1
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About this role.
MaintainX is a leading mobile-first work execution platform for industrial and frontline teams. More than 15,000 customers, including McDonalds, Michaels, Cintas, Redimix, and U.S. LBM, use MaintainX to cut unplanned downtime and run better operations, across 16 million managed assets and 92 million completed work orders.
In August 2026 MaintainX became part of Autodesk, joining Autodesk Operations Solutions, the organization unifying Autodesk’s operations platform alongside Tandem, FlexSim and Fusion Operations. Autodesk’s strategy is to converge design, make and operate into one continuous lifecycle: design an asset, build it, run it, then feed what you learn running it back into the next design. Autodesk had design and make. Operate is the phase that tells you what actually happened, and it is ours.
About the Role:
Remote (Toronto or Montreal)
As a Senior Engineer on the Quality Platform team, you’ll build the shared testing infrastructure, frameworks, and AI-assisted tooling that every engineering team at MaintainX relies on to ship quickly and confidently. Rather than owning testing for a single product area, you’ll create the platform, developer experience, and automation that make high-quality software the default across the organization.
You’ll partner closely with product engineering teams, Quality Advocates, and SRE to improve developer productivity, increase release confidence, and help shape the future of AI-powered testing at MaintainX.
What You’ll Do:
Build and evolve MaintainX’s shared quality platform, test automation frameworks, and developer tooling.
Design and develop AI-assisted test authoring infrastructure that significantly reduces test creation time while maintaining high quality.
Own CI integration standards and improve build reliability across engineering teams.
Design test harnesses for AI-powered product capabilities, including health checks, graceful degradation, quota monitoring, and adversarial testing.
Identify and eliminate systemic test debt and flaky tests before they impact engineering teams.
Build shared testing utilities and frameworks adopted across the engineering organization.
Partner with Quality Advocates, product engineering teams, and SRE to continuously improve testing strategy and platform reliability.
Evaluate emerging AI testing tools and drive adoption where they create measurable engineering impact.
About You:
Strong software engineering fundamentals with experience writing, debugging, and maintaining production application code.
Hands-on experience building and maintaining production-grade test automation frameworks using Playwright, Selenium, or similar technologies.
Experience integrating automated testing into CI/CD pipelines and diagnosing flaky or failing test suites.
Experience building or maintaining shared developer tooling, testing infrastructure, or engineering platforms — comfort with cloud infrastructure (e.g., AWS), containerized environments, and observability tooling (e.g., Datadog) is a plus.
Experience using AI development tools such as Claude Code, GitHub Copilot, Cursor, or similar to accelerate engineering workflows while applying sound engineering judgment.
Comfortable owning systems end-to-end, from architecture and implementation through production support.
Experience mentoring engineers and helping raise engineering quality across teams.
Strong spoken and written English, as you’ll collaborate daily with engineering leaders and managers across multiple teams and present technical priorities to executive stakeholders.
Nice to Have:
Experience building test harnesses for AI-powered features, including health checks, circuit-breakers, adversarial testing, or resilience testing.
Experience building or maintaining autonomous or agentic AI systems.
Experience evaluating AI-powered developer tools and integrating them into engineering workflows.
Experience building observability and quality metrics into testing platforms.
Experience working with large-scale developer platforms supporting multiple engineering teams.
Our mission is to keep the physical world running. Factories, fleets, hospitals and campuses stay up because the people who maintain them have tools worth using. That is what we build.
Compensation and benefits. Base pay is one part of the package. Depending on the role, compensation may also include commission, an annual bonus and equity. Benefits differ by country. For roles in the United States, Autodesk’s benefits are described at benefits.autodesk.com. For roles in Canada and other countries, the plan differs on health coverage, retirement and leave, and your recruiter will walk you through it.
Belonging. We take pride in a culture where everyone can thrive. More at autodesk.com/company/global-belonging. More on where this is going: Autodesk CEO Andrew Anagnost on building the future of connected operations, and AOS SVP Stephen Hooper on welcoming MaintainX to Autodesk.
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Sample interview questions
I would begin by identifying common testing workflows and pain points, then provide versioned libraries, clear interfaces, templates, documentation, and ownership boundaries. I would instrument adoption, execution time, flakiness, and failure patterns so platform improvements are guided by measurable engineering outcomes.
I would first classify failures using execution telemetry, retry behavior, logs, traces, and environment data. I would prioritize systemic causes such as unstable test data, asynchronous waits, shared state, infrastructure instability, and nondeterministic dependencies, then establish quarantine and remediation policies without allowing retries to mask persistent defects.
I would combine deterministic contract and integration tests with evaluation datasets, safety and adversarial cases, and production health checks. The harness would monitor latency, error rates, quota usage, output quality, fallback behavior, and graceful degradation when a model or upstream provider is unavailable.
I would run a controlled evaluation against representative workflows and define success metrics such as authoring time, test quality, maintenance burden, security, and developer adoption. I would also validate data handling, reliability, and governance requirements before integrating the tool into standard engineering workflows.
I would make the preferred path easy through reusable defaults, self-service documentation, fast support, and visible metrics that demonstrate value. I would involve engineers early in design decisions, gather feedback through pilots, and use lightweight governance only where consistency or risk management requires it.
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