Senior Product Manager, Construction AI

Autodesk Autodesk · Enterprise · California, USA · Remote

Senior Product Manager to lead AI assistant workflows within Autodesk Construction Cloud, focusing on identifying customer problems, defining product strategy, and driving execution for AI-powered experiences in construction. The role requires translating construction workflows into AI product opportunities, balancing innovation with responsible AI, and collaborating with cross-functional teams.

What you'd actually do

  1. Define and own the product vision, strategy, and roadmap for Construction AI assistant workflows across Autodesk Construction Cloud
  2. Translate customer pain points, market signals, AI capabilities, and business priorities into a clear, sequenced roadmap
  3. Prioritize opportunities across answer-finding, project intelligence, workflow automation, document understanding, drawing/model interactions, and agentic actions
  4. Identify where AI can create differentiated customer value versus where deterministic workflow, rules, UX, or platform improvements are more appropriate
  5. Partner with product leadership to align Construction AI investments with Autodesk’s broader platform, data, and AI strategy

Skills

Required

  • 5+ years of product management experience in B2B SaaS, enterprise software, AI/ML products, construction technology, or related domains
  • Demonstrated experience owning product vision, roadmap, prioritization, requirements, and execution for complex products
  • Experience working with engineering, design, data science, research, and go-to-market teams in a cross-functional product development environment
  • Strong understanding of AI/ML product concepts, including LLMs, retrieval-augmented generation, prompt behavior, AI evaluation, model quality, human-in-the-loop workflows, and responsible AI considerations
  • Ability to translate ambiguous customer problems into clear product strategy, requirements, milestones, and measurable outcomes
  • Strong analytical skills and experience defining metrics to evaluate product success
  • Excellent communication and stakeholder management

Nice to have

  • construction domain expertise
  • AI assistant workflows
  • customer workflows
  • platform services
  • product execution
  • AI product opportunities
  • responsible AI practices
  • cross-functional teams
  • answer-finding
  • project intelligence
  • workflow automation
  • document understanding
  • drawing/model interactions
  • agentic actions
  • construction personas
  • project managers
  • superintendents
  • VDC/BIM teams
  • project engineers
  • estimators
  • document controllers
  • account/project admins
  • executives
  • customer discovery
  • construction documents
  • RFIs
  • submittals
  • specifications
  • issues
  • meetings
  • schedules
  • drawings
  • models
  • change orders
  • project administration
  • construction technology
  • AI assistant
  • agentic workflow
  • construction automation trends
  • competitive threats
  • voice of the customer
  • product, engineering, data science, and executive discussions
  • AI-powered assistant experiences
  • AI evaluation methods
  • prompt and agent behavior
  • retrieval quality
  • grounding
  • permissions
  • observability
  • model performance
  • AI experiences are useful in real construction practice
  • transparency
  • user control
  • provenance
  • escalation paths
  • quality thresholds
  • human-in-the-loop review
  • construction knowledge graphs
  • project context
  • workflow data
  • assistant quality and trust
  • product execution
  • roadmap tradeoffs
  • customer impact
  • technical feasibility
  • AI readiness
  • business value
  • risk
  • cost
  • adoption data
  • assistant and automation experiences
  • AI-powered assistant experiences
  • AI workflows
  • Trust, Legal, Privacy, Security, and Responsible AI teams
  • product launches
  • Product Marketing
  • Sales
  • Customer Success
  • Support
  • Enablement
  • field teams
  • success metrics for AI assistant workflows
  • adoption
  • task completion
  • answer quality
  • automation success
  • time saved
  • customer satisfaction
  • trust
  • retention
  • business impact
  • product analytics
  • customer feedback
  • model evaluation
  • support signals
  • field input
  • feedback loops
  • AI quality, usability, and workflow relevance
  • product performance
  • roadmap progress
  • risks
  • tradeoffs
  • stakeholders and senior leaders
  • clear, compelling roadmap
  • Build alignment
  • measurable progress
  • high-value assistant or agentic workflow areas
  • Improve clarity around where AI should answer, recommend, automate, or defer to a human
  • Define repeatable product patterns for trusted AI workflows
  • Increase customer confidence, usage, and adoption

What the JD emphasized

  • AI assistant workflows
  • customer workflows
  • AI/ML products
  • AI/ML capabilities
  • AI product opportunities
  • responsible AI practices
  • cross-functional teams
  • agentic actions
  • LLMs
  • retrieval-augmented generation
  • prompt behavior
  • AI evaluation
  • model quality
  • human-in-the-loop workflows
  • responsible AI considerations
  • AI-powered assistant experiences
  • AI evaluation methods
  • prompt and agent behavior
  • retrieval quality
  • grounding
  • permissions
  • observability
  • model performance
  • AI experiences are useful in real construction practice
  • transparency
  • user control
  • provenance
  • escalation paths
  • quality thresholds
  • human-in-the-loop review
  • construction knowledge graphs
  • project context
  • workflow data
  • assistant quality and trust
  • product execution
  • roadmap tradeoffs
  • customer impact
  • technical feasibility
  • AI readiness
  • business value
  • risk
  • cost
  • adoption data
  • assistant and automation experiences
  • AI-powered assistant experiences
  • AI workflows
  • Trust, Legal, Privacy, Security, and Responsible AI teams
  • product launches
  • Sales, Customer Success, Support, Enablement, and field teams
  • success metrics for AI assistant workflows
  • adoption
  • task completion
  • answer quality
  • automation success
  • time saved
  • customer satisfaction
  • trust
  • retention
  • business impact
  • product analytics
  • customer feedback
  • model evaluation
  • support signals
  • field input
  • feedback loops
  • AI quality, usability, and workflow relevance
  • product performance
  • roadmap progress
  • risks
  • tradeoffs
  • stakeholders and senior leaders
  • clear, compelling roadmap
  • Build alignment
  • measurable progress
  • high-value assistant or agentic workflow areas
  • Improve clarity around where AI should answer, recommend, automate, or defer to a human
  • Define repeatable product patterns for trusted AI workflows
  • Increase customer confidence, usage, and adoption

Other signals

  • AI assistant workflows
  • customer workflows
  • product strategy
  • roadmap investments
  • trusted AI experiences at scale
  • AI product opportunities
  • responsible AI practices
  • cross-functional teams
  • answer-finding
  • project intelligence
  • workflow automation
  • document understanding
  • drawing/model interactions
  • agentic actions
  • LLMs
  • retrieval-augmented generation
  • prompt behavior
  • AI evaluation
  • model quality
  • human-in-the-loop workflows
  • responsible AI considerations
  • AI-powered assistant experiences
  • AI evaluation methods
  • prompt and agent behavior
  • retrieval quality
  • grounding
  • permissions
  • observability
  • model performance
  • AI experiences are useful in real construction practice
  • transparency
  • user control
  • provenance
  • escalation paths
  • quality thresholds
  • human-in-the-loop review
  • construction knowledge graphs
  • project context
  • workflow data
  • assistant quality and trust
  • product execution
  • roadmap tradeoffs
  • customer impact
  • technical feasibility
  • AI readiness
  • business value
  • risk
  • cost
  • adoption data
  • assistant and automation experiences
  • AI-powered assistant experiences
  • AI workflows
  • Trust, Legal, Privacy, Security, and Responsible AI teams
  • product launches
  • Sales, Customer Success, Support, Enablement, and field teams
  • success metrics for AI assistant workflows
  • adoption
  • task completion
  • answer quality
  • automation success
  • time saved
  • customer satisfaction
  • trust
  • retention
  • business impact
  • product analytics
  • customer feedback
  • model evaluation
  • support signals
  • field input
  • feedback loops
  • AI quality, usability, and workflow relevance
  • product performance
  • roadmap progress
  • risks
  • tradeoffs
  • stakeholders and senior leaders
  • clear, compelling roadmap
  • Build alignment
  • measurable progress
  • high-value assistant or agentic workflow areas
  • Improve clarity around where AI should answer, recommend, automate, or defer to a human
  • Define repeatable product patterns for trusted AI workflows
  • Increase customer confidence, usage, and adoption