Full Stack Engineer – Data & AI

eBay•Toronto, ON

1d•CA$110,400 - CA$147,400•Hybrid

About The Position

eBay’s Research Technology team is looking for a Full Stack Engineer – Data & AI (SE3) to join their platform development efforts. This role focuses on backend-heavy full-stack engineering with a strong emphasis on data engineering, AI/ML, and cloud-native application development. The engineer will be responsible for end-to-end feature and system ownership, including technical design, data modeling, implementation, deployment, monitoring, and continuous improvement. The role involves working with Python, Java, JavaScript/TypeScript, SQL, various databases, Docker, and Kubernetes, while also contributing to user experience development. This position is ideal for individuals comfortable across the full stack, with particular expertise in backend systems, data-intensive applications, and applied AI. The engineer will collaborate with research, data science, product, design, and engineering teams to deliver scalable, reliable, and user-friendly tools that support critical decision-making at eBay.

Requirements

5+ years of professional software engineering experience building and operating production applications or services, with strong experience in backend or full-stack development.
Strong hands-on programming experience in Python and Java, plus solid JavaScript/TypeScript skills for building backend services, APIs, data applications, and modern web experiences.
Strong SQL and relational database fundamentals, including schema design, data modeling, indexing, query optimization, and performance tuning, along with experience using NoSQL and embedded databases such as SQLite or DuckDB.
Experience building and supporting data-intensive systems, including data pipelines, transformation layers, and production services that provide data or analytical capabilities to users and applications.
Experience working with AI/ML-enabled applications in production, along with hands-on knowledge of RESTful API design, service integrations, Docker, Kubernetes, automated testing, CI/CD, and observability practices.
Comfort working in existing codebases and ambiguous problem spaces, with the ability to debug unfamiliar systems, communicate clearly with technical and non-technical partners, and translate complex software, data, and AI needs into practical solutions.

Responsibilities

Build and ship backend-heavy full-stack applications, APIs, dashboards, and internal tools that enable researchers, designers, and product partners to interact with complex data and AI-powered capabilities in safe and intuitive ways.
Design and operate production-quality services, pipelines, and data layers that ingest, transform, enrich, and expose structured and unstructured data for internal applications, workflows, and downstream systems.
Apply machine learning and AI techniques in production by integrating models, enterprise data, tools, and APIs into practical research and product workflows, including agentic and MCP-based capabilities where appropriate.
Improve the scalability, reliability, security, and observability of cloud-native systems by building, deploying, and operating containerized applications with Docker and Kubernetes.
Evolve existing codebases and architecture by debugging complex issues, optimizing data access and performance, improving test coverage, and introducing modern backend, data, and AI patterns with minimal disruption to critical workflows.
Collaborate closely with multi-functional partners across research, data science, product, design, and engineering to shape requirements, evaluate trade-offs, and deliver high-quality production systems while contributing to engineering standard methodologies.