Engineering Manager, ML Infrastructure & Operations – Toronto

Autodesk 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.

As 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.

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 have 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.

Salary 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.

We take pride in cultivating a culture of belonging where everyone can thrive.