AI Native Software Engineer, Risk

eBay•Austin, TX

2d•$98,000 - $173,600

About The Position

The AI Native Software Engineer will build and operate platforms and services that enable smarter risk decisions, seamless automation, and resilient workflows at scale. This hands-on role delivers scalable, reliable, and secure software systems with business impact across Risk Engineering. Engineers are expected to use modern AI-enabled practices where they can improve product quality, operational efficiency, and engineering productivity, and to own systems end to end from design and delivery through production operation. The role is part of Global Payments and Risk, which builds products and platforms for safer, more trusted commerce experiences using data, modern software architectures, and AI-native engineering practices. The team addresses risk challenges at scale across detection, decisioning, automation, and operational workflows. The position involves developing AI-enabled production systems, backend services, APIs, integrations, and workflows. The engineer will establish quality measures for AI behavior and improve system reliability, scalability, latency, security, and cost efficiency, while partnering with engineering, product, data science, analytics, and operations teams.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
At least 4 years of software engineering experience building and operating production systems.
Strong proficiency in at least one backend language such as Python, Java, or TypeScript.
Experience building and shipping backend services or product features, including design, implementation, testing, launch, and production support.
Experience building with modern AI capabilities in production or at meaningful scale, such as LLM-based workflows, tool calling, retrieval, structured outputs, workflow automation, or knowledge-grounded product experiences.
Experience designing reliable AI-powered workflows, including prompt or context design, output validation, fallback behavior, and iterative quality improvement.
Strong understanding of APIs, service integrations, data flows, logging, monitoring, and production debugging; familiarity with distributed systems concepts.
Ability to make sound engineering trade-offs across reliability, latency, scalability, cost, and developer velocity.
Strong written and verbal communication skills and ability to work effectively across engineering, product, design, and other cross-functional partners.

Nice To Haves

Experience building or contributing to agentic or tool-using workflows with clear guardrails and bounded autonomy.
Experience with AI-output evaluation workflows, including automated graders, benchmark tasks, error analysis, or human-in-the-loop review.
Familiarity with search, retrieval, ranking, embeddings, or other knowledge-grounded AI patterns.
Familiarity with secure execution practices, scoped credentials, auditability, and safe handling of AI-driven actions.
Experience using AI to improve engineering workflows, including development, testing, debugging, or operational tooling.
Experience instrumenting and improving systems through monitoring, experimentation, and iterative analysis of failures or regressions.
Experience in fraud, trust, payments, risk, or other decisioning-heavy domains.
Ability to collaborate effectively on small technical initiatives; mentoring experience is a plus.

Responsibilities

Design, build, test, deploy, and support production-ready AI-enabled systems that address customer and business problems in Risk Engineering.
Build end-to-end services and workflows combining LLMs, retrieval, tool use, structured outputs, deterministic logic, and human review where appropriate.
Develop scalable backend services, APIs, and integrations for risk decisioning, investigation, and automation use cases.
Translate business and operational needs into technical designs, delivery plans, and measurable outcomes.
Own a functional area end to end, including implementation, launch readiness, observability, monitoring, and continuous improvement.
Define and maintain quality bars for AI behavior using task-level evaluations, offline and online metrics, regression detection, and release criteria.
Improve reliability, scalability, latency, security, and cost efficiency of AI-enabled production systems.
Drive operational readiness through fallback strategies, rollback paths, incident handling, and runbooks.
Partner with engineering, product, data science, analytics, and operations teams to deliver robust, well-governed solutions.
Contribute code, design documents, and test plans, and raise engineering standards through reviews and documentation.