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About This Role
Job Requisition ID \#
26WD99521Position Overview
Autodesk is seeking a Vice President Enterprise AI and Analytics to lead the company's enterprise AI and data strategy. Reporting to the CIO, this leader will own the AIDA (Enterprise AI, Data \& Automation) domain and will serve as the primary architect of how Autodesk builds, governs, and compounds intelligence at enterprise scale. This leader will have full latitude to influence and shape the organization, operating model, and technical architecture needed to execute that vision.
There is real capability here and real work in flight. What this leader brings is the strategic clarity, architectural conviction, and organizational authority to take that capability to a fundamentally different level of scale and impact.
A central focus of this role is ensuring that Autodesk owns its intelligence. As AI capabilities become embedded across every major enterprise software platform, the risk of institutional knowledge migrating into vendor systems — and being sold back at a premium — is real and growing. This leader will define and execute the architecture that keeps Autodesk's AI on Autodesk's terms: built on open data foundations the company controls, governed by standards the company sets, and compounding in value inside Autodesk's walls.
Responsibilities
Intelligence Architecture \& Data Strategy
- Define and own Autodesk's enterprise intelligence architecture — the data foundations, agent infrastructure, and governance layer that sit above existing enterprise systems and connect them into a unified, reasoning whole
- Establish and drive the enterprise data strategy, including governance, quality, classification, access controls, and retention across cloud, application, and security domains
- Lead the transition from application\-centric data silos toward a unified data foundation built on open standards, ensuring Autodesk's data remains portable, owned, and available for AI reasoning across the full organization
- Define the framework for identifying where Autodesk's unique business logic — its decisions, rules, and workflows — lives inside vendor systems, and drive its extraction into open standards the company permanently owns
- Collaborate with analytics domain leaders across the company to establish common practices, shared standards, and a consistent approach to data and AI that scales beyond any single team
AI Program Delivery \& Portfolio Leadership
- Lead and accelerate the enterprise AI portfolio — moving active initiatives from pipeline through production with urgency and rigor, in close partnership with the business functions being served
- Establish portfolio governance: a consistent, transparent framework for evaluating, prioritizing, and sequencing AI initiatives based on business value, data readiness, strategic fit, and resource availability
- Drive build vs. buy vs. partner decisions with discipline — leveraging vendor AI capabilities where appropriate while protecting investment in Autodesk\-owned, differentiated intelligence
- Establish the working model for the full agent lifecycle — design, build, test, deploy, and maintain — creating the repeatable engineering discipline that makes agentic AI safe, scalable, and continuously improvable
Responsible AI \& Governance
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- Partner with Autodesk's Trust organization to adopt, adapt, and extend the enterprise AI governance framework — applying existing standards for model risk, bias evaluation, privacy\-by\-design, and ethical AI to AIDA's programs and accelerating their maturation
- Ensure AI initiatives operate in compliance with the evolving global regulatory landscape — EU AI Act, GDPR, CCPA, and emerging obligations — working in close coordination with Legal and Trust teams
- Extend and enforce policies governing what data can interact with which AI systems, including third\-party and vendor AI tools, building on the Trust team's existing framework at the architecture level
- Provide regular reporting to the CIO and executive leadership on AI portfolio health, governance posture, risk exposure, and business value realization
Leadership \& Organizational Development
- Lead, develop, and inspire a globally distributed team of data engineers, AI/ML practitioners, product managers, and program leaders — and shape the organizational structure and operating model that best executes the strategy
- Assess and evolve the team architecture as needed and build new capabilities in whatever configuration drives the best outcomes
- Recruit to close genuine capability gaps in AI architecture, ML engineering, and AI governance, while retaining and elevating the talent already delivering results
- Serve as the CIO's primary AI thought partner and trusted advisor to executive leaders across Autodesk on technology strategy, AI investment, and the future of human and digital work
- Foster a culture where engineering judgment is respected, ambition is rewarded, and the team is empowered to build things that matter
Minimum Qualifications
Experience
- 15\+ years in technology leadership, with a material portion spent owning enterprise AI, data platforms, or intelligent automation at scale — not just advising on them
- A portfolio of AI programs that have moved well beyond pilots — shipped, in production, and delivering measurable business outcomes: productivity gained, cost reduced, decisions improved
- Deep, hands\-on fluency in modern data engineering and data architecture — data modeling, pipeline design, open table formats (Iceberg, Delta Lake), cloud\-native data infrastructure, semantic and ontology layers, real\-time and batch processing — combined with the ability to extend that foundation into agentic AI systems: LLM orchestration, RAG architectures, LLMOps, and multi\-agent design patterns. This is a data engineer at their core who has evolved into AI.
- A clear, tested point of view on enterprise AI vendor strategy: what to adopt, what to own, and how to avoid paying a premium for intelligence that should have been yours
- Experience leading or rebuilding organizations through strategic transition — knows how to inherit a team, assess honestly, change what needs changing, and keep the best people through it
- Proven track record leading Data Science and Data Engineering functions at scale — not just as a technical practitioner, but as the leader accountable for the team, the platform, and the outcomes. This is the core discipline the role is built on.
Leadership
- Executive presence that works at every altitude — can set technical architecture with a principal engineer in the morning and brief the board on AI strategy in the afternoon, and be credible in both rooms
- Demonstrated ability to lead globally distributed, multi\-disciplinary teams across time zones and functions — with empathy for the people and accountability for the outcomes
- A track record of building organizational cultures where engineers want to work: where judgment is trusted, ambition is expected, and delivery is celebrated
- Comfort with ambiguity — this leader creates clarity for others, not the other way around
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!
Benefits
From health and financial benefits to time away and everyday wellness, we give Autodeskers the best, so they can do their best work. Learn more about our benefits in the U.S. by visiting https://benefits.autodesk.com/
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For U.S.\-based roles, we expect a starting base salary between $270,000 and $396,000\. 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.Equal Employment Opportunity
At Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.
Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global\-belonging
Are you an existing contractor or consultant with Autodesk?
Please search for open jobs and apply internally (not on this external site).
Salary Context
This $270K-$396K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Autodesk, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($333K) sits 52% above the category median. Disclosed range: $270K to $396K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Autodesk AI Hiring
Autodesk has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $235K - $396K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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