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Full Stack Engineer MTS 2 – AIRI

Job in Toronto, Ontario, M5A, Canada
Listing for: 0017 eBay Canada Technology
Full Time position
Listed on 2026-08-06
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Job Description & How to Apply Below

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

Full stack Engineer, AI Research, Innovation (MTS
2)

eBay is seeking a highly skilled, hands-on Full Stack Engineer (MTS
2) to join our AIRI division. This is an opportunity to build strategically important AI systems that power intelligent experiences at one of the world’s largest ecommerce platforms.

This is an individual contributor role for a strong senior engineer and technical leader who can own major AI engineering workstreams from design through production. We are looking for someone with strong backend depth, sound architectural judgment, and a full-stack mindset: someone who can work across the stack and is able or willing to contribute to front-end experiences as needed, without requiring expertise in any specific front-end framework.

In this role, you will provide technical leadership through system design, code reviews, design reviews, technical planning, mentoring, and hands-on delivery. You will work closely with Product, Research, Data Engineering, and Software Engineering teams to translate ambiguous ideas into practical, scalable, production-ready AI systems.

About the team and the role:

As a Full Stack AI Engineer (MTS
2) , you will work across the full AI lifecycle, including experimentation, prototyping, evaluation, production deployment, monitoring, and continuous improvement.

Your work will span Generative AI systems, LLM-powered applications, intelligent agents, conversational AI, retrieval-augmented generation, and agent-based architectures. You will be expected to own significant parts of the system, make sound technical tradeoffs, and help other engineers deliver high-quality AI solutions.

What you will accomplish:

  • Design, develop, and optimize scalable AI systems using Generative AI, LLMs, retrieval-augmented generation, and agent-based architectures.

  • Lead technical execution for major AI workstreams, services, or platform components from design through production deployment.

  • Build agent-led user experiences and backend systems that leverage task decomposition, memory, tool use, planning, retrieval, and orchestration.

  • Partner with Product, Research, Data Engineering, and Software Engineering teams to translate business and user needs into practical AI system designs.

  • Own architectural decisions for assigned systems or subsystems, ensuring reliability, maintainability, scalability, cost efficiency, and production readiness.

  • Contribute directly to implementation across backend services, model integration layers, APIs, orchestration services, evaluation pipelines, and observability tooling.

  • Lead and participate in design reviews, code reviews, technical planning discussions, and operational readiness reviews.

  • Help advance eBay’s internal GenAI platform through reusable components, APIs, frameworks, evaluation patterns, and engineering guidelines.

  • Define and implement approaches for AI system evaluation, including quality measurement, experimentation, regression testing, model behavior analysis, and production feedback loops.

  • Monitor and optimize AI systems in production for latency, quality, scalability, reliability, cost, and responsible AI use.

  • Break down ambiguous technical problems into clear implementation plans, milestones, risks, and tradeoffs.

  • Mentor engineers through hands-on technical guidance, implementation support, code reviews, and collaborative problem-solving.

  • Stay current on advances in LLMs, AI agents, retrieval systems, machine learning infrastructure, and emerging AI tooling,…

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