Sr. AI Engineer

eBay•Austin, TX

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.