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About This Role
Job Requisition ID \#
26WD98620Position Overview
We’re seeking a Senior Search Systems Engineer to build the intelligence and automation layer that sits on top of our marketing and AI visibility data. This role focuses on transforming structured data—produced by our data engineering and analytics systems—into decision frameworks, automated insights, and applied AI workflows that improve how our brand performs in AI\-driven discovery environments.
This role serves as the bridge between SEO and AEO strategy, analytics, and technical data implementation—helping define how search and AI visibility data should be collected, transformed, structured, monitored, and operationalized across Autodesk’s marketing ecosystem. While partnered closely with data engineering and BI teams, this role is responsible for ensuring the underlying datasets, instrumentation, custom dimensions, transformation logic, and reporting outputs accurately support SEO, AI visibility analysis, automation workflows, and decision\-making systems at scale.
Responsibilities
- Data Architecture and Instrumentation
+ Define the data, instrumentation, and custom dimensions required to support SEO, AI visibility, content performance, and entity\-level analysis across Autodesk’s marketing ecosystem
+ Partner with SQL developers, analytics engineers, and BI teams to operationalize scalable datasets, transformation logic, reporting tables, and analytical data models that support downstream reporting, automation, and decision systems
+ Shape ingestion and normalization strategies for search, behavioral, content, and AI visibility datasets across APIs, warehouses, and marketing platforms
- Data Quality and Governance
+ Ensure data quality, consistency, and governance across SEO and AI visibility reporting systems by validating metric definitions, transformation logic, and analytical outputs
+ Translate ambiguous business questions and SEO hypotheses into structured technical requirements that can be implemented across reporting pipelines, analytical systems, and automation workflows
- Analytical Systems and Evaluation Frameworks
+ Define analytical frameworks and scoring logic for evaluating brand visibility, entity coverage, content performance, and competitive presence across traditional and AI\-driven search environments
+ Develop monitoring and alerting systems that identify meaningful shifts in search visibility, AI model behavior, response patterns, or competitive movement over time
- Automation and AI Workflows
+ Build and maintain automation, prioritization models, and agent\-like systems that transform curated datasets into actionable recommendations for SEO, content, and discovery optimization
+ Translate marketing strategies and initiatives into scalable technical implementations, including rule\-based systems, experimentation frameworks, and applied AI workflows
+ Prototype and evaluate emerging tools, APIs, and frameworks related to LLM analysis, AI agents, search intelligence, and marketing automation
- Cross\-Functional Operationalization
+ Partner with search, content, analytics, engineering, and platform teams to ensure search intelligence systems and AI\-driven insights are embedded into planning, prioritization, experimentation, and execution workflows
+ Document system logic, assumptions, frameworks, and operational processes to ensure long\-term scalability, maintainability, and organizational clarity
Minimum Qualifications
- 7\+ years of experience in software engineering, applied analytics, search systems, or technical marketing roles that required designing and owning complex systems end\-to\-end
- Experience partnering with data engineering or analytics engineering teams to define transformation logic, data models, instrumentation requirements, and reporting outputs
- Strong understanding of marketing and analytics data architecture, including event\-level data, custom dimensions, warehouse modeling, and reporting layer design
- Experience working with APIs, structured datasets, and large\-scale analytical environments such as Snowflake, BigQuery, or similar cloud data platforms
- Strong proficiency in Python, SQL, or similar languages, with an emphasis on building durable systems, not one\-off analyses or prototypes
- Experience designing analytical or decision systems that sit downstream of a data warehouse, including defining business logic, evaluation frameworks, and failure modes
- Deep familiarity with search, discovery, or ranking systems (traditional or AI\-driven), and the ability to reason about probabilistic outputs, model variance, and imperfect signals
- Hands\-on experience evaluating, prototyping, or productionizing AI\-driven workflows, LLM\-based systems, or agent\-like architectures, with a pragmatic approach to risk and complexity
- Ability to bridge technical implementation and business strategy by translating analytical needs into scalable data and systems requirements
- Demonstrated experience operating in ambiguous problem spaces, where requirements were incomplete, tooling was immature, and success depended on judgment rather than predefined best practices
- Proven ability to influence cross\-functional partners by explaining technical tradeoffs clearly and pushing back when solutions are premature, fragile, or misaligned
- Track record of documenting systems, assumptions, and decisions to support long\-term maintainability and team learning
- Comfort being the first or only person in a role, and helping define what “good” looks like before metrics or playbooks exist
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 $102,000 and $182,710\. 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.
Diversity \& Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity\-and\-belonging
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Please search for open jobs and apply internally (not on this external site).
Salary Context
This $102K-$182K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 20% below the category median. Disclosed range: $102K to $182K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Autodesk AI Hiring
Autodesk has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Based in San Francisco, CA, US. Compensation range: $182K - $219K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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