Senior Search Data and Systems Engineer, Organic and Agentic Search

Autodesk Autodesk · Enterprise · San Francisco, CA +8 · Remote

Senior Search Systems Engineer to build the intelligence and automation layer on top of marketing and AI visibility data. This role focuses on transforming structured data into decision frameworks, automated insights, and applied AI workflows that improve brand performance in AI-driven discovery environments. The role serves as the bridge between SEO and AEO strategy, analytics, and technical data implementation, ensuring data supports SEO, AI visibility analysis, automation workflows, and decision-making systems at scale.

What you'd actually do

  1. Define the data, instrumentation, and custom dimensions required to support SEO, AI visibility, content performance, and entity-level analysis across Autodesk’s marketing ecosystem
  2. 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
  3. Define analytical frameworks and scoring logic for evaluating brand visibility, entity coverage, content performance, and competitive presence across traditional and AI-driven search environments
  4. Build and maintain automation, prioritization models, and agent-like systems that transform curated datasets into actionable recommendations for SEO, content, and discovery optimization
  5. Prototype and evaluate emerging tools, APIs, and frameworks related to LLM analysis, AI agents, search intelligence, and marketing automation

Skills

Required

  • 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

What the JD emphasized

  • building durable systems, not one-off analyses or prototypes
  • 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
  • Demonstrated experience operating in ambiguous problem spaces, where requirements were incomplete, tooling was immature, and success depended on judgment rather than predefined best practices
  • Comfort being the first or only person in a role, and helping define what “good” looks like before metrics or playbooks exist

Other signals

  • building agentic systems
  • evaluating LLM workflows
  • transforming data into insights and AI workflows