Sr. MTS Software Engineering - AIRI

eBay • Toronto, ON

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About The Position

eBay is seeking a dedicated, highly skilled, and technically hands-on Senior Staff Engineer, to join our AI Research and Innovation division. This is a high-impact individual contributor role focused on shaping, building, and scaling advanced AI systems at one of the world’s most established and heavily trafficked ecommerce platforms.
In this role, you will serve as a senior technical leader, hands-on architect, and individual contributor for strategic AI initiatives. You will guide the design, development, evaluation, deployment, and operation of production AI systems that power intelligent experiences across eBay.
This is not a people-management role. You will not be responsible for direct reports, performance management, or administrative team leadership. However, you will be expected to provide strong technical leadership through architecture, design reviews, code reviews, mentorship, technical strategy, cross-team influence, and hands-on execution.
You will partner closely with Product, Research, Data Engineering, Platform Engineering, and Software Engineering teams to translate ambiguous business and user needs into reliable, scalable, responsible, and production-ready AI systems.
As a Senior Staff AI Engineer, you will work across the full AI lifecycle: early exploration, rapid prototyping, system design, model and agent integration, evaluation, experimentation, production deployment, observability, and continuous improvement.
Your focus will include Generative AI systems, LLM-powered applications, intelligent agents, conversational AI, retrieval-augmented generation, agentic workflows, and multi-agent architectures. You will help define the technical direction for key AI initiatives while directly working to sophisticated engineering work.
This role requires deep hands-on engineering ability, strong architectural judgment, AI systems expertise, and the ability to lead through influence rather than formal authority. You should be comfortable operating in ambiguous problem spaces, making pragmatic tradeoffs, influencing senior partners, and helping teams turn ambitious AI ideas into durable production systems.
Our systems operate at large ecommerce scale, serve high-volume real-world user traffic, and power critical intelligent experiences across the eBay platform.

Requirements

• 10+ years of experience in software engineering, machine learning engineering, AI engineering, distributed systems, or related technical roles.
• 5+ years of focused experience developing, deploying, and operating AI-centric, ML-powered, or data-intensive systems in production environments.
• 3+ years of experience leading complex technical initiatives, setting architecture, mentoring engineers, or providing technical direction across teams.
• 2+ years of hands-on experience building agent-led systems, including LLM-based agents, autonomous workflows, task-planning systems, tool-using agents, retrieval-augmented agents, or multi-agent orchestration.
• Proven success taking AI-powered products, platforms, or intelligent systems from prototype to production with a strong emphasis on maintainability, scalability, reliability, performance, cost efficiency, and user impact.
• Deep hands-on engineering skills, with the ability to personally design and implement complex production systems.
• Strong programming skills in Java or similar JVM languages, with working proficiency in Python and ML/AI frameworks such as PyTorch, Transformers, scikit-learn, or similar tools.
• Experience designing and operating production-grade AI systems, distributed services, APIs, or platform components that serve high-volume, real-world user traffic.
• Strong understanding of Generative AI system design, including prompt orchestration, model integration, retrieval, grounding, tool use, evaluation, guardrails, observability, and production monitoring.
• Experience with conversational AI concepts, including intents, entities, dialogue flows, actions, user journeys, context management, and UX considerations for interactive systems.
• Hands-on experience with: Spring Framework or Spring Boot, Docker and Kubernetes, Distributed systems and scalable backend services, Large-scale data technologies such as Hadoop, Spark, or similar systems, REST, GraphQL, gRPC, or other service/API patterns, Production monitoring, observability, alerting, incident response, and performance optimization, CI/CD, automated testing, deployment practices, and operational support
• Strong understanding of AI system evaluation, including quality measurement, experimentation, model behavior analysis, regression testing, production feedback loops, and user-impact measurement.
• Ability to lead technical discussions, evaluate tradeoffs, influence senior partners, and communicate complex AI concepts clearly to technical and non-technical collaborators.

Nice To Haves

• Experience with C++ and CUDA for performance-critical AI, ML, inference, or systems components.
• Experience with streaming data systems such as Kafka, Flink, Beam, or Storm.
• Experience with retrieval-augmented generation, vector databases, embeddings, semantic search, ranking, knowledge-grounded AI, or information retrieval systems.
• Knowledge of tool-use frameworks, agent orchestration platforms, AI workflow automation, multi-modal model integration, or multi-agent system design.
• Experience building internal AI platforms, developer tools, reusable AI services, model-serving infrastructure, evaluation platforms, or shared ML infrastructure.
• Experience operating AI systems in high-traffic ecommerce, marketplace, search, personalization, recommendations, ads, trust, risk, payments, or customer-service environments.
• Experience influencing architecture across multiple teams or domains without relying on formal management authority.
• Experience helping with hiring, onboarding, technical mentoring, engineering standards, architecture forums, or technical growth programs.
• Experience defining responsible AI practices, model governance processes, safety evaluation, abuse-prevention patterns, or production AI monitoring standards.

Responsibilities

• Lead the architecture, design, development, and optimization of scalable AI systems using Generative AI, LLMs, retrieval-augmented generation, and agent-based architectures.
• Serve as the technical owner or technical lead for high-impact AI initiatives that span multiple services, systems, teams, or product surfaces.
• Design and build agent-led user experiences and backend systems that leverage task decomposition, memory, tool use, dynamic planning, retrieval, workflow orchestration, and multi-agent coordination.
• Translate ambiguous business, research, and product opportunities into clear technical strategies, architecture proposals, implementation plans, milestones, risks, and tradeoffs.
• Drive architectural decisions for AI-powered products and platforms, ensuring systems are reliable, maintainable, scalable, cost-effective, observable, and production-ready.
• Contribute directly to complex implementation work across backend services, AI orchestration layers, model integration systems, evaluation frameworks, APIs, data pipelines, and observability tooling.
• Partner with Product, Research, Data Engineering, Platform Engineering, and Software Engineering teams to align AI system design with user needs, business goals, platform capabilities, and operational constraints.
• Establish and promote technical standards for AI system design, agent architecture, LLM integration, evaluation, experimentation, responsible AI, production monitoring, and operational excellence.
• Lead design reviews, architecture reviews, code reviews, technical planning sessions, and production-readiness reviews for complex AI systems.
• Mentor and guide engineers through technical problem solving, design feedback, implementation support, code quality improvements, and knowledge sharing.
• Help advance eBay’s internal GenAI platform by contributing reusable components, APIs, frameworks, reference architectures, evaluation patterns, engineering guidelines, and shared services.
• Define and improve AI evaluation practices, including offline evaluation, online experimentation, regression testing, model behavior analysis, quality measurement, human feedback loops, and production feedback mechanisms.
• Monitor and optimize AI systems in production for latency, quality, scalability, reliability, availability, cost efficiency, safety, and responsible AI use.
• Identify technical risks early and drive practical mitigation plans across architecture, implementation, launch, and operations.
• Influence technical strategy across teams by aligning engineering decisions with broader organizational goals, platform direction, and long-term scalability.
• Stay ahead of advances in AI, LLMs, machine learning engineering, AI agents, retrieval systems, model serving, evaluation tooling, and emerging developer frameworks, applying a pragmatic lens to production adoption.
• Drive continuous improvement across design, implementation, evaluation, deployment, monitoring, incident response, and operational processes.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed