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

Robust 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

📌 Senior Product Manager, AI/ML Platform (Toronto)
🏢 Autodesk
📍 Toronto