Principal Applied AI Marketing Engineer
OktaSan Francisco, CaliforniaPosted 10 March 2026
Job Description
Get to know Okta
Okta is The World’s Identity Company. We free everyone to safely use any technology, anywhere, on any device or app. Our flexible and neutral products, Okta Platform and Auth0 Platform, provide secure access, authentication, and automation, placing identity at the core of business security and growth. At Okta, we celebrate a variety of perspectives and experiences. We are not looking for someone who checks every single box - we’re looking for lifelong learners and people who can make us better with their unique experiences.
Join our team! We’re building a world where Identity belongs to you.
The Role
As an Applied AI Marketing Engineer , you will be a core, hands-on builder within the new Marketing AI Spoke team. You will function as the primary product owner and technical implementer for a specific portfolio of AI agents, workflows, and low-code/no-code applications. This role requires a blend of marketing acumen, technical aptitude, and problem-solving skills to translate complex business challenges into tangible, deployed, AI-driven solutions.
You will be responsible for the full engineering lifecycle—defining, developing, testing, and deploying high-impact initiatives that directly improve our marketing productivity, operational efficiency, and buyer experiences.
Responsibilities
Marketing AI Solution Development: Patterning with TDI leveraging company-wide approved platform. Design, build, and maintain the underlying infrastructure and services required to run scalable AI applications for the Marketing organization. This includes connecting data sources, building APIs, developing front-end user interfaces (UI/UX), and engineering the backend logic.
AI Agent Workflow Engineering: Architect, code, test, and deploy AI agents and low-code/no-code workflows that solve critical marketing challenges. Ensure solutions are robust, secure, and seamlessly integrated into the existing AI stack (e.g., CRM, MAP, CMS).
Agentic Strategy Architectural Design: Act as the technical leader for your assigned portfolio, defining the architectural blueprints for new AI enabled workflows. Evaluate and recommend the appropriate technical tooling (e.g., cloud services, databases, low-code platforms) to maximize performance and minimize operational costs.
Data and Prompt Engineering: Partner with data science and marketing ops teams to ensure data pipelines support AI application needs (RAG, fine-tuning). Design and iterate on effective prompts and input-output schemas to optimize the performance and reliability of large language model (LLM) agents.
Value Measurement System Optimization: Implement performance tracking directly into deployed platforms (instrumentation). Use technical metrics (latency, uptime, error rates) and business metrics (ROI, time saved) to justify system improvements and guide the marketing AI roadmap.
What You'll Bring
A Bias for Action and Iteration: Demonstrated passion for a test-and-learn philosophy , quickly prototyping and validating solutions to drive continuous improvement.
AI-First Technology Mindset: Deep curiosity and commitment to leveraging AI and machine learning as foundational elements for building next-generation marketing tools and operational workflows.
Marketing Domain Curiosity: A strong interest in marketing dynamics, customer behavior, and business outcomes, translating technical capabilities into tangible marketing value.
Preferred Experience (A Strong Plus): Prior background in marketing operations, strategy, or experience working on tools/platforms utilized by large-scale marketing organizations.
Why You'll Love This Role
Become the Architect of Change: You won't be maintaining existing systems; you will be building a new, high-impact function from the ground up. This is a chance for true architectural ownership over the next generation of our marketing operating model.
Tangible and Immediate Impact: Your work on groundbreaking AI projects will ha ... (truncated, view full listing at source)
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