Looking for a company that inspires passion, courage and creativity, where you can be on the team shaping the future of global commerce? Want to shape how millions of people buy, sell, connect, and share around the world? If you're interested in joining a purpose driven community that is dedicated to crafting an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

The AI/ML team within eBay's Selling organization builds machine learning and generative AI systems that help sellers make confident pricing and listing decisions. We combine rigorous econometric methodology with production ML engineering — moving research from theoretical grounding through reproducible prototypes to seller-facing features used across eBay's global marketplace.

As an Applied Researcher, you will design and advance our research agenda across machine learning and generative AI, contributing to both our price guidance modeling and the team's expanding selling-agent platform. You will work with stakeholders across Product, Engineering, and Data to translate research findings into systems that reach sellers worldwide. Performance, reliability, and measurable business impact are central to this work, as it touches the pricing and selling decisions at the core of eBay's marketplace.

Problems we are working on:
- Designing price guidance systems that separate confounded marketplace correlations from true seller-controllable price elasticity.
- Building LLM-based selling agent components that route seller queries, invoke specialized ML tools, and synthesize actionable listing advice.
- Developing evaluation frameworks for generative AI systems: synthetic dataset construction, LLM-as-judge pipelines, and human adjudication protocols.
- Applying knowledge distillation to replace high-latency LLM classifiers with lightweight, cost-efficient encoder models that maintain production accuracy.
- Researching feedback loop dynamics between AI price recommendations and marketplace outcomes.

Qualifications
- Expertise in causal inference for ML: double machine learning, heterogeneous treatment effect estimation, observational study design, confounder handling.
- Hands-on experience with gradient boosting (LightGBM, XGBoost, CatBoost), including custom loss function design.
- Proficiency in advanced prompt engineering techniques: Chain-of-Thought, ReAct, few-shot calibration, structured output contracts, and context caching.
- Experience designing and deploying LLM-based classification or generation systems in production environments.
- Ability to build LLM evaluation frameworks: synthetic dataset construction, inter-annotator agreement measurement, and LLM-as-judge methodology.
- Demonstrated experience with agentic system patterns: tool-calling architectures, intent routing, multi-step reasoning, and safety/guardrail layers.
- Production-level experience taking ML research from prototype to deployed system, with accountability for KPIs and measurable business impact.
- Proficiency in Python with a modern data engineering stack (polars/pandas, pyarrow, scipy/statsmodels); familiarity with LangChain or an equivalent LLM orchestration framework.
- PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field, plus 3+ years of industry research experience; or MS in a related field with 5+ years of applied ML research experience demonstrating equivalent depth.

Bonus Points
- Experience with survival analysis or competing-risks models in marketplace or platform settings.
- Familiarity with knowledge distillation: training lightweight encoder models (BERT-class or smaller) from LLM teacher signal.
- Hands-on work with synthetic data generation pipelines for NLP: stratified sampling, proportional quota design, and silver-to-gold adjudication.
- Experience with marketplace or e-commerce economics: price elasticity estimation, two-sided market dynamics, behavioral pricing.
- Track record of applying academic ML/NLP literature to production systems.

The character & qualities that will help you succeed:
- You're energized by the gap between research prototype and production system — you close it.
- You read papers and ship code; you know which problems need first principles and which need engineering judgment.
- You earn cross-team credibility by being right frequently and communicating it clearly.
- You mentor junior researchers not because it's required, but because their velocity is your velocity.