Software Development Manager, Integration ML Models

Position Overview
Autodesk is building AI that helps architects and engineers design, build, and operate the world around them. As the Software Development Manager, you will lead the team that brings our foundation models and agentic workflows into the AEC tools customers use every day. You will pair an understanding of how architects and engineers work with a clear view of how AI will change that work, and you will lead a team that tests ideas fast, learns from what fails, and ships what works.

Join us in transforming the AEC industry and shaping the future of design and construction.

Report:
You will report to a Senior Software Development Manager in the Architecture, Engineering, and Construction (AEC) Solutions Team.

Location:

We support hybrid work, and you work near our Toronto, Canada office.

Job

Requisition  #
26WD101869

Team Scope
You Will Directly Manage Engineers Responsible For

Integrating foundation models and agentic workflows into AEC products such as Forma and Revit

Building the APIs and tools (including MCP) that let AI assistants and partners use those capabilities safely

Collecting the real-user feedback that improves our foundation models

Delivering client-side and server-side features and owning the end-to-end experience for what your team ships

Responsibilities

Lead a team that learns fast. Create an environment where the team tries new things, accepts that some will fail, and learns quickly from each attempt. Make it safe to be brave, fail fast, and have fun while doing it

Point the team at ideas worth testing. Identify plausible areas of investigation and frame them as testable hypotheses. Favor small, fast experiments (prototypes, evals, pilots with real users) over long plans, and know when to double down or stop

Know how architects and engineers actually work. Stay close to AEC practitioners and understand their real workflows, handoffs, deliverables, and pain points, so the team builds what they need rather than what is easy to build

Imagine how AI will change AEC work. Develop and communicate a point of view on how AI agents could transform the way architects and engineers work in the future, and turn that vision into concrete product bets for our integrations in Forma, Revit, and more

Make agentic development the way the team works. Guide the team in using coding agents day to day. Set norms for specifying work, reviewing agent output, testing, and guardrails so speed does not cost quality. Measure the real impact on throughput and keep improving the workflow

Close the loop with real users. Make sure our integrations capture the feedback that matters (ratings, edited layouts, confirmed outputs) and feed it back to ML to improve our foundation models. Define and track the metrics, such as adoption and usability, that show whether AI output is actually useful to practitioners

People leadership & coaching. Run regular 1:1s, performance conversations, and career development. Coach engineers as their role shifts toward problem framing, specification, judgment, and review of agent-generated work. Foster ownership, quality, and psychological safety

Deliver predictable results while experimenting. Turn product priorities into clear milestones and time-boxed bets. Manage scope and dependencies so the team delivers on schedule without squeezing out exploration

Cross-functional partnership. Work closely with Product, ML/Research, UX, Forma and Revit product teams, Data teams, and Security/Privacy to scope work, define acceptable behavior and failure modes, and deliver integrated solutions

Talent & hiring. Recruit and onboard people who are curious, AEC-literate, and fluent with AI tools, and participate in interview calibration and hiring

Minimum Qualifications

Track record of leading teams that ship software products and of coaching and growing individual contributors

Demonstrated understanding of how architects and engineers work, gained as a practitioner or by building products for AEC users

Hands‑on experience using AI and agentic tools in development, with a clear view of what works, what fails, and how to keep quality high

Enough technical fluency to review agent-generated work and discuss architecture and APIs credibly with engineers

Experience leading a team through experimentation: setting hypotheses, running fast tests, and making clear decisions from the results

Experience working with or alongside ML/AI or research teams to turn model capabilities into usable product experiences

Strong written and verbal…