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Role Description

This opportunity is for builders who thrive between ambiguity and execution. At eBay, you will help define and deliver the next wave of AI-powered marketplace experiences by turning emerging ideas into measurable outcomes. Success in this role means creating new capabilities that improve customer and business impact at scale.

We are looking for a Senior AI Engineer with a strong focus on AI research and innovation, combining deep experimentation with systems-building execution. You will own the full lifecycle of applied AI initiatives: problem framing, experimentation, prototyping, and production hardening. The role combines research depth with strong systems execution across modeling, serving, and product integration. This is not a pure research role and not a pure backend role. You will own end-to-end vertical slices and partner closely with software engineers, researchers, data engineers, product, and design.

What You Will Do

• Research and Modeling
  • Design, train, and evaluate systems across LLMs, retrieval, ranking, personalization, multimodal AI, and agent architectures.
  • Apply fine-tuning, distillation, retrieval-augmented generation (RAG), and orchestration strategies to improve quality and efficiency.
  • Run structured experiments and reason clearly about quality, latency, reliability, and cost tradeoffs.
  • Translate ambiguous product problems into modeling and system strategies that can be validated with users.
• Applied AI Systems and Platform
  • Design and build advanced AI capabilities across LLM workflows, retrieval systems, ranking, personalization, multimodal experiences, and agent-led interactions.
  • Develop reusable platform components, APIs, and best practices for AI application development.
  • Evaluate and integrate new models, tools, and frameworks with a critical, production-first lens.
  • Contribute to responsible AI practices, including safety, monitoring, and governance.
• Systems and Scaling
  • Build scalable inference pipelines and services for high-throughput, low-latency workloads.
  • Optimize batching, streaming, caching, and request orchestration in distributed and async environments.
  • Improve production systems across latency, throughput, reliability, observability, and unit economics.
  • Partner with infrastructure teams to leverage GPU-enabled and cloud-native environments effectively.
• Application Integration and Prototyping
  • Integrate AI capabilities into real APIs, applications, and user experiences.
  • Design end-to-end systems from data ingestion and model serving to user interaction.
  • Build prototypes that demonstrate user value, not only model-level metrics.
  • Own delivery from idea to deployed prototype and production iteration.

Qualifications

• 8+ years of software engineering and/or machine learning engineering experience.
• 4+ years of focused experience building and deploying AI-centric systems.
• 2+ years of hands-on experience with LLM-based agents, autonomous workflows, or multi-agent orchestration.
• Strong programming skills in Java or similar JVM languages, with working proficiency in Python.
• Production experience with modern ML tooling and frameworks (for example: PyTorch, Transformers, scikit-learn).
• Proven experience taking AI-powered products from prototype to production with strong maintainability and operational quality.
• Experience designing scalable backend services and APIs for real-world traffic.
• Familiarity with distributed systems, async processing, and reactive architectures.
• Ability to move from ambiguous problem statements to working systems with clear technical judgment.
• Strong communication skills and ability to collaborate across research, engineering, and product.

Preferred Qualifications

• Experience with Spring-based service development.
• Experience with Docker and Kubernetes in production environments.
• Familiarity with big data and processing ecosystems (for example: Spark, Hadoop).
• Experience with streaming systems (for example: Kafka, Flink, Beam).
• Experience with RAG pipelines, vector stores, tool-use frameworks, and multimodal model integration.
• Exposure to GPU optimization and performance tuning (for example: CUDA, inference optimization techniques).
• Experience building conversational AI systems (intents, entities, dialog flows, and interaction design).
• Ability to create lightweight frontend or internal tooling to accelerate prototyping and validation.

Benefits

• The base pay range for this position is expected in the range below: $156,800 - $255,300.
• Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience.
• The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable).
• A full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave).