Engineering Manager, Ml Infrastructure & Operations — Forma Generative Ai

Position OverviewAutodesk is transforming how the AEC industry designs and builds through integration of Artificial Intelligence (AI) and Machine Learning (ML) across its products, including AutoCAD, Revit, and Forma.
Job Requisition ID #26WD101527
Position OverviewAs the Engineering Manager for ML Infrastructure & Operations in Forma Generative AI, you will lead the team responsible for the systems that power our ML lifecycle, from large-scale training and experimentation through deployment, inference, and production operations. You will set technical direction for scalable, reliable, and cost-efficient ML infrastructure while partnering with platform teams on shared capabilities. You will establish the tooling, observability, and automated evaluation infrastructure needed to deliver AI models confidently and operate them reliably in production.
Your team will determine how quickly and reliably we can move from promising ML research to models operating at scale in Autodesk products. You will work closely with AI researchers, ML engineers, product teams, and platform organizations to make that possible.
Report:
You will report to the Head of Engineering, ML — Forma Generative AI.
Location:
We support hybrid work, and you work near our Toronto, Canada office. This role requires the ability to travel to team meetings, offsites, and conferences in the USA, Canada, and Europe.
Responsibilities
Lead, grow, and develop a team of ML engineers responsible for shared ML infrastructure and operations. Set clear priorities and expectations, coach engineers, and create opportunities for growth and increasing technical ownership
Own the infrastructure for large-scale ML training and experimentation, enabling researchers and ML engineers to run, scale, reproduce, and iterate on experiments efficiently
Own the infrastructure and workflows that move models from experimentation through product testing and production, enabling fast, safe, and reproducible deployment of new model versions
Define the architecture and optimization strategy for ML workloads as model size, data volume, compute requirements, and experiment velocity grow, with a focus on performance, utilization, and cost-efficiency
Own the operational health of shared ML systems in production, including observability, reliability targets, incident response, capacity planning, and continuous improvement
Partner with research engineering and platform teams to establish reusable infrastructure, deployment patterns, and production standards for ML systems
Build the infrastructure supporting model evaluation and release readiness, including automated regression testing, reproducible evaluation pipelines, versioning, lineage, and release gates, in partnership with Research, Product, and Trusted AI teams
Minimum Qualifications
BS/MS in Computer Science, Engineering, or equivalent practical experience
2+ years of engineering people-management experience, including coaching engineers, setting priorities, and driving technical delivery
Experience designing, building, and operating infrastructure for ML training, experimentation, and/or inference at scale
Experience designing and operating distributed systems in a major cloud environment (AWS, Azure, or GCP), including production observability, reliability, and capacity management
Strong software engineering, communication, and technical decision-making skills
Preferred Qualifications
Experience with distributed ML training, GPU workload orchestration, and/or high-performance inference systems
Experience building infrastructure for foundation models or other large-scale ML systems
Experience in the AEC industry, Autodesk, or AEC data and workflows
Evidence of staying current with fast-moving ML infrastructure technologies and applying new approaches pragmatically
The Ideal Candidate
You care deeply about reliability, performance, and cost efficiency across the ML lifecycle, and a low tolerance for production systems held together by hope
You communicate effectively across disciplines and levels, adapting your approach to build shared understanding with technical, product, and research audiences
You can move comfortably between people leadership, architecture, and problem-solving, making pragmatic build-vs-partner decisions
You create clarity in ambiguous technical areas by communicating effectively across research, engineering, product, and platform teams
You build teams with clear ownership, strong engineering standards, and room for engineers to grow
Learn MoreAbout AutodeskWelcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.
We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.
When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!
Salary transparencySalary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $158,000 and $231,000. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.
BelongingWe take pride in cultivating a culture of belonging where everyone can thrive. Learn more here:
In-Person Onboarding and Identity VerificationThis role may require in-person onboarding and/or in-person ID verification.
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