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Define and execute the ML platform roadmap to accelerate data discovery, experimentation, deployment, inference, and AI integration. Partner with engineers and data scientists to remove workflow friction, build interoperable, scalable platform capabilities (inference, observability, foundation model support), analyze usage, and drive adoption through strong product experiences and cross-functional execution.
Job Requisition ID #
26WD99140
Position Overview
As an ML Platform Product Manager, you will help define and execute the roadmap for the Autodesk Machine Learning Platform (AMP), focused on making it easier and faster for Autodesk’s ML engineers and data scientists to build, test, deploy, and manage AI-powered solutions at scale.
You will partner closely with platform engineers, ML engineers, and data scientists to understand how they work, identify the tools and processes that create friction, and prioritize platform improvements that increase productivity. Your focus will span end-to-end workflows including data exploration, experimentation, model deployment, inference, monitoring, and downstream AI integration.
This role is centered on improving the experience of Autodesk’s internal AI/ML practitioners. You will help simplify complex technical processes, improve interoperability across tools and systems, and establish scalable workflows that allow teams to spend less time navigating infrastructure and more time developing AI capabilities.
We are looking for a technically fluent, execution-oriented Product Manager who is passionate about improving complex AI/ML workflows and building scalable platform capabilities for technical users. You do not need to write machine learning code, but you should understand how modern AI/ML systems are developed and be comfortable discussing technical requirements, constraints, and tradeoffs with engineering teams.
Responsibilities
- Define and execute roadmap priorities for the Autodesk ML Platform with a focus on practitioner productivity, workflow acceleration, interoperability, and platform adoption
- Develop a deep understanding of ML engineers and data scientists as internal users, using practitioner feedback and platform data to identify pain points and prioritize improvements
- Improve end-to-end AI/ML workflows across:
- Data discovery and exploration
- Experimentation, training, and model iteration
- Model deployment and lifecycle management
- Model serving and inference
- Monitoring and observability
- Downstream AI integration
- Partner closely with ML engineers, data scientists, and platform engineers to identify workflow bottlenecks, understand root causes, and translate practitioner needs into clear product requirements and priorities
- Help shape scalable platform capabilities related to:
- Model deployment and serving
- Inference services
- AI/ML observability
- Experiment and model lifecycle management
- Foundation model and generative AI support
- Large language model (LLM) workflows
- Drive platform interoperability across data platforms, ML tooling, experimentation environments, deployment systems, observability solutions, and downstream applications
- Establish intuitive “golden path” workflows that simplify common AI/ML use cases while maintaining flexibility for advanced practitioner needs
- Prioritize competing user needs and platform investments based on practitioner impact, technical feasibility, dependencies, and broader product priorities
- Analyze platform usage, adoption, workflow friction, and practitioner feedback to identify opportunities and drive continuous product improvement
- Define measurable outcomes for platform capabilities, including improvements in adoption, workflow efficiency, experimentation velocity, deployment experience, and practitioner productivity
- Collaborate with cross-functional product and engineering teams to drive platform adoption through strong product experiences, onboarding, documentation, and measurable workflow improvements
- Communicate product priorities, requirements, decisions, and tradeoffs clearly across technical and cross-functional stakeholder groups
- Stay current on evolving AI platforms, MLOps/LLMOps, inference, foundation models, generative AI, cloud infrastructure, and developer tooling trends
Minimum Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
- 3+ years of product management experience focused on ML platforms, AI infrastructure, developer platforms, or technical workflow products
- Strong understanding of:
- Machine learning systems and lifecycle workflows
- Data, cloud, and analytics ecosystems
- Model deployment, inference, and operational AI systems
- Developer or technical practitioner workflows
- Familiarity with modern AI approaches including foundation models, generative AI, and large language models (LLMs)
- Experience working closely with ML engineers, data scientists, platform engineers, software engineers, or other technical practitioners to understand user needs and translate them into product capabilities
- Demonstrated ability to identify friction in complex technical workflows and translate those insights into clear product priorities and measurable improvements
- Experience prioritizing competing needs and making product tradeoffs across user experience, technical complexity, scalability, and platform strategy
- Ability to engage credibly in technical discussions and understand system-level concepts and constraints without needing to be the primary technical implementer
- Demonstrated ability to drive execution in ambiguous, technically complex, and fast-changing environments
- Strong product judgment, communication, prioritization, and cross-functional collaboration skills
- Experience with AI/ML platforms, developer tools, cloud infrastructure, data platforms, or products designed for technical users is highly desirable
Learn More
About Autodesk
Welcome 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 transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $122,000 and $179,300. 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.Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging
In-Person Onboarding and Identity Verification
This role may require in-person onboarding and/or in-person ID verification.
Are you an existing contractor or consultant with Autodesk?
Please search for open jobs and apply internally (not on this external site).
Autodesk Toronto, Ontario, CAN Office
661 University Ave, Toronto, ON, Canada, M5G 1M1
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