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About This Role
Job ID 26WD100188 Department Applied AI Engineering Job Requisition ID #
26WD100188 This role can be remote within the U.S. or Canada, though San Francisco is the preferred location
About The Team
At GET (Growth Experience Technology), we fuel sustainable growth by rethinking how customers discover, buy, and use Autodesk. Our focus is creating a seamless, data-driven experience that feels simple, predictable, and tailored to customer needs—while also making it easier and more efficient for our teams to support them.
To get there, we're building smarter digital experiences, strengthening our platforms, and making our sales and marketing efforts much more efficient and connected. While the product experience will play a key role, a real unlock will come from the technologies that power better customer insights, more personalized engagement, and faster sales cycles.
What you’ll do
The GET Applied AI organization is looking for a seasoned Senior Manager / Senior Principal of Applied AI Engineering to lead the application of AI, machine learning, and advanced analytics across Autodesk’s B2C business. This role will play a critical leadership role in shaping how Autodesk uses data and AI to build the eCommerce and personalization platforms of the future.
Applied AI Engineers at Autodesk are both data/research scientists and software engineers, who develop and implement machine learning models and algorithms. Unlike other companies that separate these roles, our Applied AI Engineers work on projects from ideation to implementation.
As a Sr. Principal Applied AI Engineer at Autodesk, you will be responsible for the development and training of cutting-edge machine learning models and algorithms that can effectively leverage our users’ platform activities and industry trends to create personalized recommendations that are delivered to our users at the optimal moment.
As a senior leader within Applied AI, you will set technical and strategic direction, lead a portfolio of high-impact AI/ML initiatives, develop talent, and partner with senior business and technology leaders to translate Autodesk’s business priorities into scalable AI capabilities.
You will be responsible for advancing machine learning solutions that leverage customer behavior, product engagement, commerce signals, and industry trends to create more relevant and personalized customer experiences. You will also help establish the organizational practices, technical standards, and operating mechanisms needed to scale Applied AI across Autodesk.
Responsibilities
- Set the Applied AI Strategy – Define and drive the vision, roadmap, and investment priorities for key data science and machine learning capabilities across Autodesk’s B2C business, including personalization, recommendation systems, segmentation, forecasting, experimentation, churn prediction, product analytics, and customer insights.
- Lead a Portfolio of High-Impact Initiatives – Own multiple complex, cross-functional AI/ML programs from strategy through production and business adoption. Establish clear priorities, success metrics, operating rhythms, and accountability to ensure the team delivers measurable customer and business outcomes.
- Build and Develop a High-Performing Team – Provide leadership, coaching, and career development for Data Scientists and Machine Learning Engineers. Create an environment that attracts strong technical talent, raises the bar for execution, and enables individuals to grow in both technical depth and leadership.
- Provide Technical Leadership – Establish technical direction and engineering standards for machine learning systems, model development, experimentation, evaluation, production deployment, and MLOps. Guide teams through complex technical decisions while ensuring solutions are scalable, maintainable, and aligned with Autodesk’s broader technology architecture.
- Expand Autodesk’s ML Capabilities – Identify emerging opportunities where AI and machine learning can materially improve the customer experience or business performance. Guide the design and development of scalable end-to-end ML platforms and pipelines that enable personalization and intelligent decision-making at scale.
- Drive Experimentation at Scale – Establish robust experimentation and measurement practices that increase development velocity while maintaining scientific rigor. Enable teams to rapidly test hypotheses, evaluate model performance, quantify incremental impact, and translate learnings into product and business decisions.
- Influence Senior Leadership – Serve as a trusted thought partner to senior leaders across Product, Engineering, eCommerce, Marketing, Finance, and other functions. Frame ambiguous business problems, identify opportunities where AI can create value, recommend investment priorities, and influence strategic decisions through data.
- Drive Cross-Functional Execution – Initiate and lead large-scale, complex strategic initiatives spanning multiple teams and organizations. Build alignment across technical and business stakeholders, resolve ambiguity and competing priorities, and drive programs through execution and adoption.
- Champion a Data- and AI-Driven Culture – Evangelize best practices in applied AI, machine learning, experimentation, and evidence-based decision-making. Help shape Autodesk’s broader approach to responsible, scalable, and business-oriented use of AI.
- Translate Technology into Business Impact – Develop compelling narratives that connect data, model performance, customer behavior, and financial outcomes. Communicate complex technical concepts clearly to executive, business, and technical audiences and ensure AI investments are tied to measurable impact.
- Raise the Organizational Bar – Establish reusable frameworks, processes, and best practices that improve how Applied AI teams prioritize work, evaluate opportunities, design solutions, and measure results. Foster collaboration and knowledge sharing across Data Science, ML Engineering, and adjacent technical organizations.
Minimum Qualifications
- Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or another related quantitative or technical field.
- 10+ years of experience in Data Science, Machine Learning, Applied Research, or a related technical discipline, with significant experience delivering production-scale machine learning solutions.
- Demonstrated experience leading teams, technical organizations, or major cross-functional programs, including setting strategy, establishing priorities, and driving execution across multiple simultaneous initiatives.
- Experience developing technical talent through coaching, mentorship, technical leadership, performance management, or people management.
- Proven ability to lead technical strategy, machine learning system design, and optimization of production-scale ML infrastructure, including model deployment, model evaluation, data processing, monitoring, debugging, and model optimization.
- 7+ years of experience with software or machine learning system design and architecture, including launching and operating production systems.
- Deep hands-on experience with machine learning, data mining, information retrieval, recommendation or personalization systems, and applied statistical methods.
- Strong expertise in A/B testing, experimental design, causal reasoning, and measurement.
- Strong understanding of statistics, probability, customer and product analytics, and financial or business modeling.
- Demonstrated ability to translate ambiguous business challenges into data and AI strategies and connect technical investments to measurable customer and business impact.
- Exceptional communication and stakeholder-management skills, with demonstrated ability to influence senior technical and business leaders.
Preferred Qualifications
- MS or PhD in Data Science, Statistics, Computer Science, Machine Learning, Engineering, or another quantitative discipline.
- 15+ years of experience in machine learning, data science, or related disciplines, ideally including experience in eCommerce, personalization, growth, digital products, or consumer-facing platforms.
- 5+ years of experience in technical or people leadership, including building and developing high-performing Data Science and/or Machine Learning Engineering teams.
- Experience defining multi-year AI/ML strategies and translating them into organizational roadmaps, investment priorities, and measurable business outcomes.
- Experience hiring, developing, and retaining senior Data Scientists, Machine Learning Engineers, and technical leaders.
- Experience operating effectively in a matrixed environment and influencing senior executives across Product, Engineering, Marketing, Finance, and business organizations.
- Hands-on experience with cloud machine learning platforms such as AWS or Azure and strong knowledge of modern MLOps, model governance, observability, and production ML best practices.
- Experience building personalization, recommendation, ranking, forecasting, customer lifecycle, or next-best-action systems at significant scale.
See how we’re building a diverse workforce and a culture of belonging to give more people the chance to imagine, design, and create a better world. Learn about all things #AutodeskLife by following us on Instagram, Twitter, Facebook, LinkedIn, and Glassdoor.
At Autodesk, we are 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
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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 $178,875 and $320,650. 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
In-Person Onboarding and Identity Verification
This role may require in-person onboarding and/or in-person ID verification.
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 $178K-$320K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($249K) sits 18% above the category median. Disclosed range: $178K to $320K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Autodesk AI Hiring
Autodesk has 5 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Product Manager. Positions span Boston, MA, Montreal, Quebec, Canada, New York, NY, US. Compensation range: $266K - $320K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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