Principal User Researcher, AI

📍 Job Overview

Job Title: Principal User Researcher, AI

Company: Autodesk

Location: San Francisco, California, United States

Job Type: Full-Time

Category: User Research / AI / Product Strategy

Date Posted: May 13, 2026

Experience Level: Principal (8+ years)

Remote Status: Hybrid

🚀 Role Summary

• Define and lead the research strategy for emerging AI-enabled and agentic customer experiences, focusing on how AI can better connect user intent to outcomes within Autodesk's design and make workflows.

• Establish and operationalize user-centered evaluation frameworks for AI systems, extending beyond technical accuracy to encompass trust, ambiguity handling, usability, and cognitive outcomes.

• Drive deep understanding of customer cognition, trust, behavior, and workflow patterns through mixed-methods research, translating these insights into actionable signals for product, evaluation, and system design decisions.

• Serve as a strategic partner to Product, Engineering, Data Science, and Design teams, influencing long-term product direction and ensuring AI experiences are intelligent, reliable, trustworthy, and grounded in real customer needs.

📝 Enhancement Note: This role is positioned as a Principal User Researcher, indicating a significant level of autonomy, strategic influence, and leadership in defining research practices for cutting-edge AI products. The focus on "agentic capabilities" and "adaptive, AI-enabled systems" suggests a forward-thinking approach to user research, requiring expertise beyond traditional UX research to incorporate AI evaluation methodologies and understanding of human-AI interaction dynamics. The role requires not just execution but also the definition of new practices.

📈 Primary Responsibilities

• Research Strategy & System-Level Impact:

  • Define and lead end-to-end research strategy for emerging AI-enabled and agentic customer experiences.
  • Establish research as a core input into product, evaluation, and system design decisions, not solely downstream validation.
  • Translate customer cognition, trust, behavior, and workflow patterns into actionable signals that inform roadmap and experience optimization.
  • Help shape how Autodesk defines and measures customer value within agentic systems and workflows.
  • Influence long-term product and experience direction through research-backed insights and frameworks.

• Agentic AI Evaluation & Feedback Systems:

  • Design and operationalize user-centered evaluation frameworks that extend beyond technical accuracy into trust, ambiguity handling, usability, and cognitive outcomes.
  • Partner with Product, Engineering, and AI/ML teams to align technical performance with customer experience quality.
  • Build and evolve continuous feedback loops that connect real-world usage signals to product iteration.
  • Help define evaluation as an ongoing product capability that supports adaptive, learning-oriented experiences.
  • Ensure evaluation frameworks are scalable, reusable, and aligned with iterative product development cycles.

• Behavioral Insight & Workflow Intelligence:

  • Lead research that uncovers latent customer needs through behavioral data, workflow analysis, and real-world usage patterns.
  • Identify high-impact workflow and automation opportunities across design and make experiences.
  • Design research approaches that support continuous learning and iterative product improvement.
  • Translate insights into product opportunities that reduce friction, improve efficiency, and strengthen customer outcomes.
  • Partner cross-functionally to embed insight generation into product experiences and systems.

• Trust, Adoption & Human-AI Interaction:

  • Define and lead research on trust formation, calibration, and recovery within AI-enabled customer experiences.
  • Identify interaction patterns that drive adoption—or create barriers to adoption—across different customer segments.
  • Develop frameworks and guidance for human-AI collaboration experiences that evolve with users over time.
  • Partner with Product and Design teams to ensure onboarding, interaction design, and system behavior support sustained engagement.
  • Translate trust and adoption research into actionable product principles and measurable success criteria.

• Human Capability & Workforce Impact:

  • Lead research initiatives exploring how AI-enabled systems influence customer learning, decision-making, and long-term capability development.
  • Define and operationalize approaches for evaluating skill development and workflow transformation within Design + Make industries.
  • Design and test interaction patterns that support learning, professional growth, and effective decision-making.
  • Ensure product decisions consider accessibility, equitable AI use, and broader workforce impact considerations.
  • Contribute to strategic thinking around the future of AI-enabled workflows and industry evolution.

• Cross-Functional Leadership & Influence:

  • Serve as a strategic partner to Product, Engineering, Data Science, and Design teams in shaping priorities and decisions.

  • Drive alignment across research, product development, experience readiness, and optimization efforts.

  • Elevate research impact through clear and compelling communication with senior and executive stakeholders.

  • Contribute to thought leadership around human-centered AI research and adaptive customer experiences.

  • Mentor and guide researchers while helping scale research quality and organizational influence.

  • Shape how Autodesk approaches research for next-generation customer experiences and agentic capabilities over time.

📝 Enhancement Note: The responsibilities highlight a strategic, leadership-oriented role focused on the evolving landscape of AI in product development. The emphasis on "defining research strategy," "establishing frameworks," and "influencing long-term direction" indicates a need for proactive problem-solving and thought leadership. The detailed breakdown across AI evaluation, behavioral insights, human-AI interaction, and workforce impact underscores the complexity and breadth of the role, requiring a deep understanding of both user psychology and AI system design principles.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Master's or Ph.D. in Human-Computer Interaction (HCI), Cognitive Psychology, Anthropology, Sociology, or a related field is highly advantageous for a Principal-level researcher, especially in AI.

Experience: 8+ years of experience in user research, human-computer interaction, cognitive science, or a related field, with a demonstrated track record of driving product impact.

Required Skills:

• Research Strategy Definition: Proven ability to define and lead research strategy in complex, ambiguous domains, particularly within AI-enabled, data-driven, or systems-oriented products.

• Mixed-Methods Research Expertise: Deep expertise in qualitative, quantitative, and behavioral data analysis, including study design, execution, and synthesis.

• AI Evaluation Frameworks: Experience designing and operationalizing research frameworks that influence product direction and decision-making at scale, specifically for AI/ML systems.

• Human-AI Interaction: Strong understanding of human-AI interaction principles, trust formation, decision-making processes, and/or cognitive systems.

• Cross-Functional Collaboration: Proven experience working effectively with Product Management, Engineering, Design, and Data Science teams to drive product outcomes.

• Insight Translation & Influence: Demonstrated ability to translate research insights into meaningful product, system, or business outcomes, with strong stakeholder influence skills.

• Communication & Storytelling: Excellent verbal and written communication skills, with the ability to articulate complex findings clearly and compellingly to diverse audiences, including executive stakeholders.

• Adaptability & Experimentation: Ability to thrive in fast-evolving environments focused on experimentation, prototyping, and discovery, particularly in new and ambiguous problem spaces.

Preferred Skills:

• Experience with agentic AI systems and their implications for user workflows.

• Familiarity with statistical modeling and advanced data analysis techniques for behavioral data.

• Experience in the Design & Make industries (e.g., AEC, Manufacturing, Media & Entertainment).

• Knowledge of AI ethics, bias detection, and fairness in AI systems.

• Experience with generative AI and its user experience implications.

• Mentorship or leadership experience within a research team.

📝 Enhancement Note: The "Minimum Qualifications" are extensive and clearly indicate a Principal-level role requiring significant strategic thinking and impact. The emphasis on "defining strategy," "operationalizing frameworks," and "influencing decisions at scale" points to a need for a researcher who can shape the research function itself, not just execute studies. The inclusion of "AI-enabled, data-driven, or systems-oriented products" and "human-AI interaction" are critical keywords for candidates.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies in AI Research: Showcase 2-3 comprehensive case studies detailing your involvement in research projects, ideally related to AI, complex systems, or novel user experiences.

• Strategic Impact: For each case study, clearly articulate the research problem, your strategic approach, methodologies used (qualitative, quantitative, behavioral), key insights, and the tangible impact on product decisions, strategy, or business outcomes.

• Methodological Breadth: Demonstrate proficiency across a range of research methods, including generative (e.g., interviews, ethnography), evaluative (e.g., usability testing, A/B testing), and analytical (e.g., behavioral data analysis, log analysis).

• AI/System Focus: Highlight experience with research for complex systems, AI/ML products, or data-driven features, emphasizing how you navigated ambiguity and influenced system-level design.

• Deliverables: Include examples of key deliverables such as research plans, reports, personas, journey maps, and executive summaries that effectively communicate findings and recommendations.

Process Documentation:

• Research Plan Development: Evidence of creating detailed research plans that outline objectives, target users, methodologies, timelines, and success metrics for complex projects.

• Evaluation Framework Design: Examples of designing and implementing evaluation frameworks for AI systems, focusing on aspects like trust, reliability, usability, and user cognition.

• Insight Synthesis & Actionability: Demonstrate how you synthesize data from multiple sources to create actionable insights and clear recommendations for cross-functional teams.

• Continuous Improvement Processes: Showcase experience in establishing feedback loops and iterative research processes to support ongoing product development and optimization.

📝 Enhancement Note: For a Principal User Researcher role, especially in AI, a portfolio is critical. It needs to go beyond simply listing projects and should deeply illustrate strategic thinking, methodological rigor, and demonstrable impact. The emphasis on "AI research," "strategic impact," and "evaluation frameworks for AI systems" are key differentiators that candidates should highlight. The ability to document and demonstrate process is as important as the insights themselves.

💵 Compensation & Benefits

Salary Range: The provided salary range for this role in San Francisco, CA, is between $117,000 and $210,540 USD per year.

Benefits:

• Health Benefits: Comprehensive medical, dental, and vision insurance plans.

• Financial Benefits: Potential for annual cash bonuses and stock grants, contributing to long-term financial well-being.

• Time Away: Generous paid time off, holidays, and potentially other leave options.

• Everyday Wellness: Programs and resources designed to support employee well-being.

• Professional Development: Opportunities for continuous learning and skill enhancement.

Working Hours: The standard working hours are 40 hours per week, aligning with typical full-time employment. The role is hybrid, allowing for flexibility in balancing on-site and remote work, which can support work-life integration.

📝 Enhancement Note: The salary range provided ($117,000 - $210,540 USD) is a strong indicator of a Principal-level position in a high-cost-of-living area like San Francisco. This range is competitive for senior research roles, especially those with a specialized focus like AI. The benefits listed are standard for a large tech company and should be considered comprehensive. The hybrid work arrangement offers flexibility, a key consideration for many professionals.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Technology, specifically focused on Design and Make workflows across various sectors including Architecture, Engineering, Construction (AEC), Manufacturing, and Media & Entertainment. Autodesk's mission is to help innovators turn their ideas into reality and transform how things are made.

Company Size: Autodesk is a large, established technology company. While the exact number of employees isn't provided in the raw data, it's recognized as a significant player in its industry, suggesting a robust organizational structure with various departments and specialized teams.

Founded: Autodesk was founded in 1982, indicating a long history and deep experience in the CAD and design software market, now evolving into digital transformation and AI integration.

Team Structure:

• Reporting: The role reports to the Sr. Manager, AI User Research, suggesting a dedicated AI User Research team within a larger Product or R&D organization.

• Cross-Functional Collaboration: The Principal User Researcher will work closely with Product Management, Design, Engineering, Data Science, and AI/ML teams, indicating a highly collaborative environment where research insights are integrated into the product development lifecycle.

• Specialization: The team likely comprises researchers with diverse specializations, with this role focusing on the strategic impact of AI and agentic capabilities.

Methodology:

• Human-Centered AI: A core methodology is integrating human-centered research principles into the development of AI-enabled systems, ensuring that AI solutions are designed for real customer needs, trust, and usability.

• Data-Driven Decision Making: Emphasis on using both qualitative and quantitative data, including behavioral analysis, to inform product strategy and development.

• Iterative Development & Experimentation: The role operates in an environment that values experimentation, prototyping, and continuous feedback loops to refine AI experiences.

Company Website: https://www.autodesk.com/

📝 Enhancement Note: Understanding Autodesk's industry focus on "Design and Make workflows" is crucial. This role will involve researching how AI can enhance complex professional processes, not just consumer applications. The company's long history suggests a blend of established processes and a drive for innovation, particularly in AI. The described collaborative structure is typical of modern tech companies aiming for agile product development.

📈 Career & Growth Analysis

Operations Career Level: Principal User Researcher. This level signifies a senior individual contributor role, expected to operate with a high degree of autonomy. It involves defining strategic research directions, influencing product roadmaps, and potentially mentoring junior researchers. The focus on AI and agentic systems positions this as a cutting-edge area within the broader UX research field.

Reporting Structure: Reports to the Sr. Manager, AI User Research. This indicates a clear line of reporting within a specialized team, allowing for focused guidance and development within the AI research domain.

Operations Impact: The impact of this role is significant, directly influencing the development of next-generation AI-enabled customer experiences. By ensuring these experiences are intelligent, reliable, trustworthy, and grounded in customer needs, the researcher contributes to product adoption, customer satisfaction, and ultimately, Autodesk's competitive advantage in the AI space. This includes optimizing workflows, enhancing decision-making, and shaping the future of professional tools.

Growth Opportunities:

• Deep Specialization in AI Research: Opportunity to become a leading expert in human-centered AI research, evaluation, and human-AI interaction, a highly in-demand skill.

• Strategic Influence: Potential to shape research methodologies and product strategy for AI at a global technology company, influencing the direction of Autodesk's AI initiatives.

• Leadership & Mentorship: Possibility of mentoring junior researchers, contributing to the growth of the AI research team, and potentially moving into management roles over time.

• Industry Thought Leadership: Opportunity to contribute to the broader discourse on human-centered AI through internal presentations, external publications, or conference participation.

• Cross-Functional Leadership: Develop strong partnerships across Product, Engineering, and Design, gaining a holistic understanding of product development and expanding influence across the organization.

📝 Enhancement Note: The "Principal" title is key here, denoting a senior individual contributor role with significant strategic responsibility. The growth opportunities are framed around deepening expertise in a high-demand field (AI Research), influencing strategy, and potential leadership, which are attractive for senior-level professionals. The impact on product adoption and competitive advantage highlights the business criticality of the role.

🌐 Work Environment

Office Type: Hybrid. This indicates a blend of remote work and in-office presence. Autodesk likely offers a modern office environment designed to foster collaboration and innovation when employees are on-site.

Office Location(s): Primarily based in San Francisco, CA, with potential for light travel across North America for meetings and conferences. The San Francisco office is likely a hub for R&D and strategic teams.

Workspace Context:

• Collaborative Spaces: The office environment is expected to support collaborative work, team meetings, and brainstorming sessions, especially for hybrid teams.

• Technology & Tools: Access to standard professional office technology, with specific emphasis on the research tools and platforms necessary for AI user research and analysis.

• Team Interaction: Opportunities for in-person interaction with team members and cross-functional colleagues during office days, fostering stronger working relationships and facilitating spontaneous collaboration.

Work Schedule: The role is full-time with a standard 40-hour work week. The hybrid arrangement provides flexibility, allowing researchers to structure their week to balance focused remote work with collaborative in-office sessions, accommodating the needs of research projects and team dynamics.

📝 Enhancement Note: The hybrid model is a significant aspect of the work environment, requiring individuals to be proactive in managing their time and communication across remote and in-office settings. The mention of "light travel" suggests opportunities for in-person collaboration with teams in other Autodesk locations or at industry events.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will likely review applications and resumes, focusing on alignment with the required years of experience, AI research background, and key skills.

• Portfolio Review: Candidates will be asked to submit a portfolio showcasing their work. This is a critical step where the depth of their research strategy, methodological expertise, and impact on AI products will be assessed.

• Interviews with Cross-Functional Teams: Expect interviews with peers (other researchers), product managers, designers, and engineering leads. These interviews will assess research craft, strategic thinking, collaboration skills, and ability to influence.

• Hiring Manager Interview: A discussion with the Sr. Manager, AI User Research, focusing on strategic research vision, leadership potential, and cultural fit.

• Executive/Senior Stakeholder Interview: Potentially an interview with a more senior leader to assess strategic alignment and long-term vision for AI research at Autodesk.

Portfolio Review Tips:

• Curate for AI Impact: Select case studies that best demonstrate your experience with AI, agentic systems, or complex data-driven products.

• Highlight Strategic Contributions: Clearly articulate your role in defining research strategy, influencing product direction, and driving measurable outcomes, not just executing research tasks.

• Showcase Methodological Rigor: Explain your choice of methodologies and how they were applied to address specific research questions, especially in ambiguous AI domains.

• Quantify Impact: Wherever possible, use metrics and data to demonstrate the business or product impact of your research.

• Tell a Story: Structure your case studies as compelling narratives that highlight problem, process, insights, and resolution.

Challenge Preparation:

• AI Research Scenario: Be prepared for a hypothetical scenario where you might need to research a new AI feature or agentic capability. Think about how you would approach defining the research strategy, identifying key questions, and choosing appropriate methods.

• Strategic Problem-Solving: Anticipate questions about how you would handle complex, ambiguous research problems and how you would influence stakeholders with differing opinions.

• Human-AI Interaction Principles: Review your understanding of trust, explainability, bias, and user control in AI systems, and be ready to discuss how research can address these.

📝 Enhancement Note: The interview process for a Principal role will be rigorous, with a strong emphasis on the portfolio. Candidates must be prepared to articulate their strategic thinking and demonstrate how they've driven impact. The "challenge preparation" section is crucial for demonstrating proactive thinking about how to tackle AI-specific research problems.

🛠 Tools & Technology Stack

Primary Tools:

• User Research Platforms: Tools for participant recruitment, scheduling, and remote usability testing (e.g., UserTesting.com, Lookback, Maze).

• Qualitative Analysis Software: Tools for thematic analysis, affinity mapping, and qualitative data synthesis (e.g., Dovetail, NVivo, Miro).

• Quantitative Analysis Tools: Proficiency in statistical software or libraries for analyzing survey data, behavioral data, and experimental results (e.g., SPSS, R, Python libraries like Pandas, NumPy, SciPy).

• Survey Tools: Platforms for designing and deploying surveys (e.g., Qualtrics, SurveyMonkey, Google Forms).

• Collaboration & Documentation: Tools for team collaboration, note-taking, and documentation (e.g., Miro, Confluence, Google Workspace).

Analytics & Reporting:

• Data Visualization Tools: Experience with tools to create compelling dashboards and reports (e.g., Tableau, Power BI, Looker).

• Behavioral Analytics Platforms: Familiarity with tools that track user interactions within software (e.g., Google Analytics, Adobe Analytics, Amplitude, Mixpanel).

• A/B Testing Frameworks: Understanding of experimental design and analysis for A/B testing.

CRM & Automation:

• While not a direct requirement for a researcher, understanding how user feedback and insights can be integrated into CRM systems or product feedback platforms (e.g., Salesforce, Jira, Zendesk) can be beneficial for driving action.

📝 Enhancement Note: While the job description doesn't list specific tools, a Principal User Researcher in AI would be expected to be proficient in a range of qualitative and quantitative research tools, as well as data analysis and visualization platforms. The ability to work with behavioral analytics tools is particularly important given the role's focus on workflow intelligence and adaptive systems.

👥 Team Culture & Values

Operations Values:

• Human-Centered Innovation: A deep commitment to understanding and serving customer needs, ensuring that AI and technology advancements are focused on delivering genuine value.

• Data-Driven Decision Making: Valuing rigorous data analysis and empirical evidence to inform strategic choices and product development.

• Collaboration & Partnership: Fostering a collaborative environment where teams work together across disciplines to achieve common goals.

• Curiosity & Continuous Learning: Encouraging a culture of exploration, experimentation, and ongoing learning, especially in the rapidly evolving field of AI.

• Impact & Accountability: Focusing on delivering tangible outcomes and taking ownership of research's contribution to product success and business goals.

Collaboration Style:

• Cross-Functional Integration: Actively partnering with Product, Engineering, Design, and Data Science to embed research insights throughout the product lifecycle.

• Open Communication: Encouraging transparent and constructive dialogue, sharing findings and perspectives openly to build consensus and drive informed decisions.

• Process Improvement: A proactive approach to refining research processes and collaborating with other teams to optimize how insights are generated and utilized.

• Mentorship and Knowledge Sharing: A willingness to share expertise, mentor junior colleagues, and contribute to the collective growth of the research practice.

📝 Enhancement Note: Autodesk's culture emphasizes innovation, customer focus, and collaboration. For a Principal User Researcher, embodying these values means not only conducting research but also evangelizing user needs, facilitating cross-functional understanding, and contributing to a culture that prioritizes data-informed decisions and continuous improvement in the AI space.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating AI Ambiguity: The rapidly evolving nature of AI and agentic systems presents inherent ambiguity in research objectives, methodologies, and expected outcomes. This role requires comfort with uncertainty and the ability to define research in uncharted territory.

• Defining & Measuring AI Success: Establishing clear, user-centered metrics for AI performance beyond technical accuracy (e.g., trust, cognitive load, workflow efficiency) and operationalizing their measurement can be challenging.

• Influencing Technical Teams: Translating user needs and research findings into actionable requirements for AI/ML engineers and data scientists requires strong communication and a deep understanding of technical constraints and possibilities.

• Scaling Research Practices: As AI capabilities expand, scaling research methodologies and ensuring consistent quality across multiple product teams can be a significant organizational challenge.

• Ensuring Ethical AI & Trust: Researching and advocating for ethical AI practices, fairness, transparency, and building user trust in AI systems are complex but critical responsibilities.

Learning & Development Opportunities:

• Deep Dive into AI/ML: Opportunities to deepen understanding of AI/ML concepts, agentic systems, and generative AI, enabling more effective research and collaboration.

• Advanced Research Methodologies: Potential to explore and implement novel research methods tailored for AI, such as causal inference, reinforcement learning-based user studies, or advanced simulation techniques.

• Strategic Product Leadership: Developing skills in strategic product planning, roadmap influence, and cross-functional leadership within a major technology organization.

• Industry Conferences & Publications: Encouragement to attend and present at leading UX and AI research conferences, contributing to personal and organizational thought leadership.

• Mentorship Programs: Access to senior leaders for mentorship, guidance on career progression, and development of leadership capabilities.

📝 Enhancement Note: The challenges are directly tied to the cutting-edge nature of AI research. Candidates should be prepared to address these with examples of how they've overcome similar obstacles. The growth opportunities are substantial, focusing on specialization in a high-demand field and strategic influence.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to define a research strategy for a completely new and ambiguous product area, particularly one involving AI or complex systems. What was your approach, and what were the outcomes?" (Focus on strategic thinking, ambiguity navigation, and impact.)

• "How would you approach evaluating the 'trustworthiness' of an AI-generated design suggestion for a professional user? What methods would you use, and how would you measure success?" (Tests understanding of AI evaluation and human-AI interaction.)

Company & Culture Questions:

• "What interests you specifically about Autodesk's mission and its application of AI in design and make workflows?" (Shows research into the company and alignment with its goals.)

• "How do you see the role of user research evolving in the age of agentic AI and adaptive systems?" (Assesses forward-thinking perspective and alignment with the role's strategic nature.)

Portfolio Presentation Strategy:

• Structure Your Narrative: For each case study, clearly define the problem, your strategic approach, the research methods used, key insights, and the tangible impact you drove.

• Focus on "Why" and "So What": Explain why you chose certain methods and so what the implications of your findings were for the product and business.

• Highlight AI/System Expertise: Emphasize any experience with AI, complex systems, or data-driven products and how your research contributed to their development or improvement.

• Be Prepared for Deep Dives: Anticipate detailed questions about your methodologies, decision-making process, and the challenges you faced.

• Connect to Autodesk: If possible, draw parallels between your past work and Autodesk's industry, product areas, or AI initiatives.

📝 Enhancement Note: Interview preparation should focus on demonstrating strategic thinking, deep methodological expertise in AI research, and strong influence skills. The portfolio is central, so candidates must be able to articulate their contributions and impact clearly and concisely.

📌 Application Steps

To apply for this Principal User Researcher, AI position:

• Submit Your Application: Complete and submit your application through the Autodesk careers portal. Ensure your resume highlights relevant experience in user research, HCI, cognitive science, and AI.

• Craft a Compelling Portfolio: Prepare a portfolio that showcases your most impactful research projects, with a strong emphasis on AI, complex systems, or strategic product influence. Clearly articulate your role, methodology, insights, and outcomes.

• Tailor Your Resume: Optimize your resume to include keywords from the job description such as "AI User Research," "Agentic Systems," "Mixed-Methods Research," "Human-AI Interaction," "Research Strategy," and "Stakeholder Influence." Quantify achievements whenever possible.

• Prepare for Portfolio Presentation: Be ready to walk through your portfolio during interviews, clearly explaining your contributions and the impact of your work. Practice articulating your strategic approach and research rationale.

• Research Autodesk and AI Initiatives: Understand Autodesk's mission, its products, and its current focus on AI. Prepare to discuss how your skills and experience align with their strategic goals and the specific challenges of this role.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.