Applied Researcher 2

eBay•Toronto, ON

About The Position

The Search Ranking and Monetization team sits at the core of eBay’s marketplace, powering the search and advertising experiences that connect millions of buyers and sellers every day. As the largest contributor to eBay’s advertising program, the team focuses on redefining e-commerce advertising through cutting-edge machine learning, large-scale experimentation, and a strong focus on customer value. We are looking for applied researchers who are energized by the scale and technical complexity of online advertising and search. In this role, you will design and deploy advanced models that shape how listings are ranked, how ads are served, and how buyers discover the inventory they love. You will partner closely with product, engineering, and analytics teams, and you will have opportunities to share your work internally and in external forums and conferences. This role sits within the Search Ranking and Monetization organization and directly impacts the quality, relevance, and monetization of eBay’s search experience.

Requirements

MS or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience in applied machine learning.
1–3 years of relevant industry experience with a PhD, or 3–5 years with an MS, in areas such as online advertising, ranking, recommendation systems, targeting systems, fraud detection, or related domains.
Hands-on experience applying machine learning techniques (for example, classification, regression, ranking, recommendation) in large-scale, real-world systems, including designing and evaluating models in production.
Proficiency with big data tools and technologies such as Hadoop, SQL, and Spark, and comfort working with large, complex datasets to build reliable data pipelines.
Strong programming skills in Python or R and in at least one of Java, Scala, or C/C++, with a track record of building high-quality, maintainable, production-grade software.
A record of scientific impact demonstrated by 2 or more related publications in reputable conferences or journals, and the ability to communicate research findings clearly to both technical and non-technical audiences.

Responsibilities

Deliver scientifically robust solutions that meaningfully improve key buyer and seller outcomes, such as search relevance, engagement, conversion, and marketplace efficiency.
Design, build, and iterate on machine learning models and data pipelines that power ranking, recommendation, targeting, and advertising products in eBay search.
Partner with cross-functional teams to define problem statements, validate hypotheses, run online experiments, and translate research into scalable, production-ready systems.
Promote and standardize scientific methodologies, experimentation practices, and evaluation frameworks across Search Ranking and Monetization and adjacent teams.
Share technical and research contributions through internal forums and external conferences, helping position eBay as a leader in e-commerce advertising and large-scale machine learning.
Continuously deepen your expertise in large-scale ML, big data technologies, and online experimentation, while mentoring and learning from peers in a collaborative, supportive environment.