Software Engineer II - AI Enablement

StubHub•Los Angeles, CA

14h•Hybrid

About The Position

StubHub is on a mission to redefine the live event experience on a global scale. Whether someone is looking to attend their first event or their hundredth, we’re here to delight them all the way from the moment they start looking for a ticket until they step through the gate. The same goes for our sellers. From fans selling a single ticket to the promoters of a worldwide stadium tour, we want StubHub to be the safest, most convenient way to offer a ticket to the millions of fans who browse our platform around the world. We’re seeking a Software Engineer – AI Enablement to help shape how StubHub adopts and scales AI across the company. This role is about making AI practical, safe, and valuable by empowering teams to experiment confidently, automate intelligently, and deliver better outcomes faster. The mission of this role is to help build StubHub’s AI capability as both a delivery engine and a force multiplier for the company. You will own high-impact AI solutions end to end while contributing to the shared platform capabilities, reusable patterns, and internal tooling that make them scalable, reliable, and repeatable. Working across functions and teams, you will solve meaningful problems while strengthening the foundation that enables AI to redefine how work gets done at StubHub. Success in this role is not measured by layering AI onto existing processes or shipping isolated prototypes, but by building AI-native capabilities and new ways of operating that drive meaningful gains in speed, scale, and quality.

Requirements

Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.
4+ years of experience building and shipping production software, AI-enabled products, internal tools, or workflow systems.
Experience turning ambiguous problems into practical technical solutions and driving them through implementation and iteration.
Strong software engineering fundamentals, with proficiency in Python and experience working across APIs, backend services, and integrations.
Experience building or supporting production systems using containerization, cloud infrastructure, and modern deployment practices.
Familiarity with CI/CD, version control, and standard engineering practices for reliability and maintainability.
Experience evaluating and applying AI models, tools, or platforms based on business fit, technical performance, and implementation practicality.
Ability to work independently, manage multiple priorities, and operate effectively in ambiguous environments.
Strong analytical and problem-solving skills, with the ability to use data and feedback to improve deployed systems over time.
Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.

Nice To Haves

Experience contributing to successful company-wide AI adoption by helping teams integrate AI into real workflows at scale.
Native development experience, especially in mobile app environments or workflows where AI capabilities need to integrate across web and native surfaces.
Familiarity with privacy, security, and responsible-use considerations when building AI systems.

Responsibilities

Own the design and delivery of AI solutions, platform capabilities, and internal tooling that create meaningful leverage across StubHub.
Translate ambiguous business and workflow problems into practical AI solutions that can be shipped, adopted, and improved over time.
Build reusable infrastructure, services, and patterns that make AI solutions scalable, reliable, and repeatable across teams and workflows.
Work with cross-functional partners to identify valuable opportunities and turn them into production-grade AI capabilities.
Select and operationalize the right models, tools, and platforms based on impact, practicality, and long-term fit.
Produce the documentation, guidance, and shared assets needed to make adoption easier and reduce duplicated effort across the company.
Help define strong engineering standards for how AI systems are built, evaluated, shipped, and improved in practice.
Track performance and business outcomes of what is deployed, and use those learnings to improve both individual solutions and the shared foundation.
Continuously evaluate new AI industry developments and apply them where they can materially improve speed, scale, or quality.