Full Stack Engineer MTS 2 – AIRI

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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, applying a practical lens to production use.

• Contribute to continuous improvement across design, implementation, deployment, monitoring, and operational processes.

What you will bring: 

• 8+ years of experience in software engineering, machine learning engineering, AI engineering, or related technical roles.

• 4+ years of focused experience developing, deploying, and operating AI-centric or ML-powered systems in production environments.

• 1–2+ years of experience leading technical initiatives, owning major engineering workstreams, mentoring engineers, or providing technical direction.

• Hands-on experience building Generative AI, LLM, retrieval-augmented generation, conversational AI, or agent-led systems.

• Experience taking AI-powered features or services from prototype to production with attention to maintainability, scalability, performance, reliability, and user impact.

• Strong hands-on engineering skills, with the ability to contribute directly to complex system design and implementation.

• Strong programming skills in Java or similar JVM languages, with working proficiency in Python and familiarity with ML frameworks such as PyTorch, Transformers, and scikit-learn.

• Experience designing and operating production-grade backend systems, distributed services, APIs, or AI platforms that serve real-world user traffic.

• Full-stack mindset with the ability or willingness to contribute to front-end development using modern web technologies; expertise in a specific front-end framework is not required.

• Strong understanding of AI system evaluation, including offline evaluation, online experimentation, model behavior analysis, quality metrics, and feedback loops.

• Hands-on experience with:

  • Spring Framework or Spring Boot

  • Docker and Kubernetes

  • Large-scale data technologies such as Hadoop or Spark

  • Distributed systems and scalable backend services

  • Production monitoring, observability, and performance optimization

  • CI/CD, testing, deployment, and operational support practices

• Ability to evaluate technical tradeoffs and communicate complex AI concepts clearly to technical and non-technical collaborators.

Bonus Qualifications

• Experience with C++ or CUDA for performance-critical AI or ML components.

• Familiarity with streaming data systems such as Kafka, Flink, Beam, or Storm.

• Experience with vector databases, embeddings, semantic search, ranking systems, knowledge grounding, and retrieval-augmented generation.

• Knowledge of agent orchestration frameworks, tool-use patterns, workflow automation, multi-modal models, or multi-agent systems.

• Experience building internal AI platforms, reusable AI services, developer tools, or shared ML infrastructure.

• Experience supporting high-traffic ecommerce, marketplace, search, personalization, recommendations, trust, ads, or customer-service AI systems.

• Experience improving engineering practices through reusable patterns, documentation, testing frameworks, evaluation harnesses, or operational playbooks.

Additional Details

This job posting relates to an existing vacancy within eBay.

eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at talent@ebay.com. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility.

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