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About This Role
Job Requisition ID \#
26WD100188*L'affichage de poste en français suivra / The French job posting follows.*
26WD100188, Senior Principal AI/ML Developer
Position Overview
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.
Our GET Applied AI org is looking for a seasoned Sr. Principal Scientist/Machine Learning Developer to drive data\-informed decisions for Autodesk B2C business. You will play a critical role in helping Autodesk build the eCommerce platforms of the future. Data Scientists/MLE are both data/research scientists and software developers, who develop and implement machine learning models and algorithms. Unlike other companies that separate these roles, our Data Scientists work on projects from ideation to implementation. As a Senior Principal Data Scientist 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.
Responsibilities
- Act as a champion for a data\-driven culture, evangelizing best practices and shape the direction of key data science areas \- segmentation, recommendation systems, forecasting, product analytics, churn prediction and insights
- Identify, design, prototype, and build scalable end\-to\-end ML pipelines that enhance Autodesk’s eCommerce personalization at scale
- Develop and implement robust experimentation frameworks to increase velocity while maintaining scientific rigor, enabling rapid iteration and deployment of personalization models
- Craft compelling Stories and make logical recommendations based on insights of data and ML models
- Partner with cross\-functional teams to initiate, lead and drive to completion large\-scale/complex strategic projects for teams, departments and the company
- Act as a thought partner to senior cross\-functional leaders to prioritize/scope projects, provide recommendations, and evangelize data\-driven business decisions in support of strategic goals
- Mentor and guide junior data scientists/MLE by helping with project planning, technical decisions, and code and document review
Minimum Qualifications
- Bachelor's degree in data science, statistics, computer science, a related technical field
- 10 years of experience leading technical project strategy, ML design, and optimizing industry\-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
- 7 years of experience with design and architecture; and testing/launching software products
- 10\+ years of hands\-on experience with data mining, and information retrieval
- Experience in A/B testing and experimental design
- Strong understanding of statistics, probability, and financial modeling
- Ability to translate data insights into business impact
Preferred Qualifications
- MS or PhD in a quantitative discipline: data science, statistics, computer science, a related technical field
- 15\+ years of hands\-on experience in machine learning design, development and deployment, ideally in eCommerce domain
- Hands\-on experience with cloud machine learning platform (AWS, Azure) and MLOps best practices
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26WD100188, Développeur principal senior en IA/ML
Aperçu du Poste
Chez GET (Growth Experience Technology), nous favorisons une croissance durable en repensant la manière dont les clients découvrent, achètent et utilisent les solutions Autodesk. Notre objectif est de créer une expérience fluide, axée sur les données, qui soit simple, prévisible et adaptée aux besoins des clients, tout en permettant à nos équipes de les accompagner plus facilement et plus efficacement. Pour y parvenir, nous développons des expériences numériques plus intelligentes, renforçons nos plateformes et rendons nos efforts de vente et de marketing bien plus efficaces et coordonnés. Si l’expérience produit jouera un rôle clé, c’est avant tout grâce aux technologies permettant une meilleure compréhension des clients, un engagement plus personnalisé et des cycles de vente plus rapides que nous parviendrons à nous démarquer véritablement.
Notre organisation GET Applied AI recherche un scientifique principal senior / développeur en apprentissage automatique expérimenté pour piloter les décisions fondées sur les données au sein de l’activité B2C d’Autodesk. Vous jouerez un rôle essentiel en aidant Autodesk à construire les plateformes de commerce électronique de demain. Les scientifiques des données / développeurs en apprentissage automatique (MLE) sont à la fois des scientifiques spécialisés dans les données et la recherche, et des développeurs logiciels, qui conçoivent et mettent en œuvre des modèles et des algorithmes d’apprentissage automatique. Contrairement à d’autres entreprises qui séparent ces fonctions, nos data scientists travaillent sur des projets de la conception à la mise en œuvre. En tant que data scientist principal senior chez Autodesk, vous aurez la responsabilité du développement et de l’entraînement de modèles et d’algorithmes d’apprentissage automatique de pointe, capables d’exploiter efficacement les activités de nos utilisateurs sur la plateforme ainsi que les tendances du secteur afin de créer des recommandations personnalisées qui seront proposées à nos utilisateurs au moment le plus opportun.
Responsabilités
- Être le fer de lance d’une culture axée sur les données, promouvoir les meilleures pratiques et définir l’orientation des domaines clés de la science des données : segmentation, systèmes de recommandation, prévision, analyse produit, prédiction du taux de désabonnement et insights
- Identifier, concevoir, prototyper et construire des pipelines d’apprentissage automatique de bout en bout et évolutifs qui améliorent la personnalisation du commerce électronique d’Autodesk à grande échelle
- Développer et mettre en œuvre des cadres d’expérimentation robustes pour accroître la vélocité tout en conservant une rigueur scientifique, permettant ainsi l’itération et le déploiement rapides de modèles de personnalisation
- Élaborer des récits convaincants et formuler des recommandations logiques basées sur les enseignements tirés des données et des modèles d’apprentissage automatique
- Collaborer avec des équipes interfonctionnelles pour lancer, diriger et mener à bien des projets stratégiques complexes et à grande échelle pour les équipes, les services et l’entreprise
- Jouer le rôle de partenaire stratégique auprès des responsables interfonctionnels de haut niveau pour hiérarchiser et définir la portée des projets, formuler des recommandations et promouvoir des décisions métier fondées sur les données, en soutien aux objectifs stratégiques
- Encadrer et guider les data scientists et MLE juniors en les aidant dans la planification des projets, les décisions techniques, ainsi que la révision du code et de la documentation
Qualifications Minimales
- Licence en science des données, statistiques, informatique ou dans un domaine technique connexe
- 10 ans d’expérience dans la direction de la stratégie de projets techniques, la conception d’apprentissage automatique (ML) et l’optimisation d’infrastructures ML à l’échelle industrielle (par exemple : déploiement de modèles, évaluation de modèles, traitement des données, débogage, réglage fin)
- 7 ans d’expérience en conception et architecture, ainsi qu’en tests et lancement de produits logiciels
- Plus de 10 ans d’expérience pratique en exploration de données et en recherche d’informations
- Expérience en tests A/B et en conception expérimentale
- Solide compréhension des statistiques, des probabilités et de la modélisation financière
- Capacité à traduire les enseignements tirés des données en impact commercial
Qualifications Souhaitées
- Master ou doctorat dans une discipline quantitative : science des données, statistiques, informatique ou un domaine technique connexe
- Plus de 15 ans d’expérience pratique dans la conception, le développement et le déploiement de systèmes d’apprentissage automatique, idéalement dans le domaine du commerce électronique
- Expérience pratique des plateformes d’apprentissage automatique dans le cloud (AWS, Azure) et des meilleures pratiques MLOps
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 $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).
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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Autodesk AI Hiring
Autodesk has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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