ML Infra & Ops Eng Manager – Hybrid Toronto

ML Infra & Ops Eng Manager – Hybrid Toronto

Toronto Full-Time 135000 - 165000 $ / year (est.) Hybrid
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At a Glance

  • Tasks: Lead a team to develop and manage ML lifecycle systems for innovative AI projects.
  • Company: Autodesk, a leader in transforming the AEC industry with cutting-edge technology.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Be part of a collaborative environment focused on innovation and impact.
  • Why this job: Join a dynamic team and shape the future of AI in architecture and engineering.
  • Qualifications: Experience in ML infrastructure and strong leadership skills required.

The predicted salary is between 135000 - 165000 $ per year.

Autodesk is transforming the AEC industry by integrating AI and ML across AutoCAD, Revit, and Forma. As the Engineering Manager for ML Infrastructure & Operations in Forma Generative AI, you will lead the team responsible for ML lifecycle systemsβ€”from training and experimentation to deployment, inference, and production operations. You will set technical direction for scalable, reliable, and cost-efficient ML platforms and collaborate with researchers, product teams, and platform groups to drive.

ML Infra & Ops Eng Manager – Hybrid Toronto employer: Autodesk

Autodesk is an exceptional employer that fosters a dynamic and inclusive work culture, offering flexible hybrid or remote work options for the AI Compute Strategy Specialist role. Employees benefit from continuous growth opportunities through collaboration with innovative teams, while contributing to impactful projects in the AI space. With a focus on analytics and financial forecasting, Autodesk empowers its workforce to drive meaningful change in various sectors, making it a rewarding place to advance your career.

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Contact Details:

Autodesk Recruitment Team

We think you need these skills to ace ML Infra & Ops Eng Manager – Hybrid Toronto

Machine Learning Lifecycle Management
AI Integration
Technical Direction Setting
Scalable ML Platforms
Reliable ML Systems
Cost-Efficient Solutions
Collaboration with Researchers