MTS 1, Software Engineer

eBay•San Jose, CA

1d•$172,000 - $229,600•Remote

About The Position

Global Payments and Risk builds products and platforms that enable safer, more trusted commerce experiences. We use data, modern software architectures, and AI-native engineering practices to solve risk challenges at scale across detection, decisioning, automation, and operational workflows. This team operates highly scalable systems with meaningful customer and business impact. We are seeking a talented Software Engineer to build and operate the platforms and services that enable smarter risk decisions, seamless automation, and resilient workflows at scale. In this hands-on role, you will deliver scalable, reliable, and secure software systems that drive meaningful business impact across Risk Engineering. Engineers in this organization are expected to use modern AI-enabled practices effectively. In this role, that includes applying AI where it can improve product quality, operational efficiency, and engineering productivity. This is a hands-on, applied software engineering role with end-to-end ownership for builders who want to design, ship, and operate real systems in production.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
5+ years of software engineering experience building and operating production systems.
Strong proficiency in the backend languages such as Java, Nodejs, TypeScript and Python.
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.
Demonstrated ability to make sound engineering trade-offs across reliability, latency, scalability, cost, and developer velocity.
Strong written and verbal communication skills, with the 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 working with evaluation workflows for AI outputs, 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 is a plus.
Demonstrated 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 solve real customer and business problems in Risk Engineering.
Build end-to-end services and workflows that combine LLMs, retrieval, tool use, structured outputs, deterministic logic, and human review where appropriate.
Develop scalable backend services, APIs, and integrations that support risk decisioning, investigation, and automation use cases.
Translate business and operational needs into clear 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 through task-level evaluations, offline and online metrics, regression detection, and release criteria.
Improve reliability, scalability, latency, security, and cost efficiency of AI-enabled systems in production.
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 high-quality code, design documents, and test plans, and raise engineering standards through thoughtful reviews and documentation.