Distinguished Applied AI Engineer, AI Transformation

Autodesk Autodesk · Enterprise · AMER - United States - California - San Francisco - One Market

This role focuses on defining and accelerating the adoption of AI across Autodesk's go-to-market (GTM) ecosystem. It involves technical leadership, architecture, and influence to embed AI into marketing, sales, commerce, and customer success systems. The engineer will guide build-vs-buy decisions, operationalize responsible AI, and identify scalable AI patterns like RAG and recommendation systems to drive business outcomes.

What you'd actually do

  1. Define Autodesk’s AI Strategy for GTM Platforms
  2. Guide Build-vs-Buy Decisions
  3. Guide AI Adoption
  4. Understand and Influence GTM Platform Architecture
  5. Operationalize Responsible and Trusted AI

Skills

Required

  • Advanced degree (or equivalent experience) in Computer Science, Engineering, Data Science, or a related field
  • 15-20 years’ experience in a software company
  • 5+ years in AI/ML, data strategy, or platform-driven transformation roles, ideally spanning both technical and business contexts
  • Proven ability to operationalize technology in large, matrixed organizations
  • Proven ability to connect AI capabilities to business value and drive cross-functional execution
  • Deep understanding of AI ecosystems (LLMs, GenAI, Information Retrieval, Knowledge Management an experimentation) and their operationalization within modern SaaS architectures
  • Familiarity with Responsible AI frameworks and compliance considerations
  • Strong analytical and decision-making skills related to build-vs-buy tradeoffs, vendor evaluation, and platform integration
  • Excellent communication and influence skills across executive and technical audiences

Nice to have

  • Experience leading AI strategy or platform development within large-scale digital, customer experience, or GTM organizations
  • Hands-on familiarity with Autodesk’s or similar enterprise GTM systems (Salesforce, Segment, Gainsight, etc.)
  • Understanding of ML platform operations, data architecture, and instrumentation design
  • Demonstrated success scaling AI prototypes into production and measuring their sustained impact
  • Expert in data-informed decision making

What the JD emphasized

  • operational outcome driven adoption of AI
  • durable architectures
  • reusable patterns
  • operating models
  • trusted technical authority
  • accountable to real business outcomes
  • platform architecture
  • business outcomes
  • embedded technology stack
  • responsibly and effectively into each
  • specialized ML systems where deep expertise creates differentiated value
  • foundation and commodity models
  • embed AI into everyday workflows
  • operationalize Responsible and Trusted AI
  • transparency, fairness, and explainability
  • Trust and Legal teams
  • scalable AI patterns
  • retrieval-augmented generation (RAG)
  • recommendation systems
  • conversational frameworks
  • deployed consistently
  • Measure Business Impact
  • instrumentation, success metrics, and evaluation frameworks
  • tangible outcomes
  • conversion lift, engagement quality, and customer lifetime value
  • Align and Evangelize Across Stakeholders
  • cohesive execution across the customer journey
  • Drive AI Fluency Across GET and GTM Partners
  • enablement programs, documentation, and forums
  • responsible, scalable use of AI
  • Advanced degree (or equivalent experience) in Computer Science, Engineering, Data Science, or a related field
  • 15-20 years’ experience in a software company
  • 5+ years in AI/ML, data strategy, or platform-driven transformation roles
  • technical and business contexts
  • operationalize technology in large, matrixed organizations
  • connect AI capabilities to business value
  • cross-functional execution
  • Deep understanding of AI ecosystems (LLMs, GenAI, Information Retrieval, Knowledge Management an experimentation)
  • operationalization within modern SaaS architectures
  • Responsible AI frameworks
  • compliance considerations
  • build-vs-buy tradeoffs
  • vendor evaluation
  • platform integration
  • communication and influence skills across executive and technical audiences
  • leading AI strategy or platform development
  • large-scale digital, customer experience, or GTM organizations
  • Autodesk’s or similar enterprise GTM systems
  • ML platform operations
  • data architecture
  • instrumentation design
  • scaling AI prototypes into production
  • measuring their sustained impact
  • data-informed decision making
  • Strategic technologist
  • Fluent in both the language of business outcomes and the realities of systems architecture
  • innovate internally
  • integrate best-in-class tools
  • Guiding teams forward through credibility and trust
  • speed and responsibility must coexist
  • AI’s greatest potential lies in amplifying human and organiza

Other signals

  • AI strategy for GTM platforms
  • Guide build-vs-buy decisions
  • Operationalize Responsible and Trusted AI
  • Accelerate adoption through reusable patterns (RAG, recommendation systems, conversational frameworks)
  • Measure business impact of AI initiatives