Applied Researcher 3 at eBay

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

04 Jul, 26

Salary

0.0

Posted On

05 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Scala, PyTorch, Large Language Models, Recommender Systems, Data Analysis, Spark, Distributed Systems, A/B Testing, Causal Inference, Auction Optimization, Ranking, Forecasting, Technical Leadership, Experimentation

Industry

technology;Information and Internet

Description

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all. About the team and the role: Advertising is one of the fastest growing areas in eBay which in some ways, is crafting the future direction of eBay. Digital advertising as an industry is growing rapidly, and the landscape is shifting as ecommerce advertisers are finding better value with ecommerce companies like eBay. As they shift budget from the duopoly of Google and Facebook, it builds a huge opportunity for eBay. Advertising is also amplifying eBay’s ecommerce by providing a tool for our sellers to move inventory and for buyers, by surfacing high quality items. This team builds end-to-end ML and data-driven advertising systems that power both ad serving and advertiser-side optimization. We develop large-scale recommendation models to achieve marketplace monetization and bring relevant buyer experiences, while also providing intelligent guidance to help advertisers optimize their targeting, bids, budgets, inventory and business goals through automation, machine learning including GenAI. This is a high-impact and fast-growing area with significant business potential, requiring work with massive datasets and advanced machine learning techniques across ranking, forecasting, optimization, and experimentation. As an Applied Researcher within our Advertising team, you will play a pivotal role in developing machine learning models and algorithms to guide advertisers optimally. This role will also include a scope positioned around data analysis. Your work will directly impact our guidance systems, enhancing ad performance and delivering actionable insights. You will lead our efforts in data analysis, uncovering trends, and driving data-informed decisions that support our advertisers' success. What you will accomplish: End-to-End ML System Ownership: Own the design, development, and deployment of large-scale machine learning systems for advertising. Own critical components of advertiser guidance, ranking, or optimization systems, ensuring robustness, scalability, and measurable business impact Problem Framing & Technical Leadership: Translate ambiguous business challenges into well-defined ML problems. Drive solution design, modeling strategy, and experimentation frameworks across projects. Advanced Modeling & Innovation: Develop and apply pioneering techniques in areas such as LLMs, recommender systems, auction optimization, and causal inference. Push the frontier of applied ML to improve advertiser outcomes and platform efficiency. Cross-team Influence: Collaborate across organizations (product, engineering, data, infra) to shape roadmaps and influence system design. Act as a key technical partner in defining long-term advertising strategy. Research & External Impact: Drive high-impact research initiatives. Publish in top-tier conferences (e.g., KDD, WWW, NeurIPS) and represent eBay in the broader research community. Mentorship & Technical Leadership: Mentor researchers and engineers, provide technical guidance, and raise the overall bar of the team. Lead design reviews and promote innovative practices in ML development and experimentation. What you will bring: Education: Ph.D. with 5+ years, or M.S. with 8+ years of experience in Computer Science, Statistics, Mathematics, or a related field. Deep Applied ML Experience: Proven track record of delivering impactful machine learning solutions in production at scale. Strong experience in advertising systems, recommender systems, search, or large-scale NLP/LLM applications. Technical Leadership: Demonstrated ability to lead sophisticated projects end-to-end, influence technical direction, and make architectural decisions. Advanced Technical Expertise: Expert-level proficiency in Python/Scala or similar. Deep experience with modern ML stacks (e.g., PyTorch, distributed training, LLM frameworks). Strong understanding of large-scale data processing (Spark, distributed systems). Experimentation & Causal Thinking: Experience designing online/offline experiments (A/B testing, causal inference) and translating results into product decisions. Communication & Collaboration: Outstanding ability to communicate with senior partners and influence multi-functional teams. Able to bridge research, engineering, and product effectively. Additional Details This job posting relates to an existing vacancy within eBay. 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. If you have a need that requires accommodation, please contact us at talent@ebay.com. We will make every effort to respond to your request for accommodation as soon as possible. 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Responsibilities

You will design, develop, and deploy large-scale machine learning systems to optimize advertising performance and advertiser guidance. Additionally, you will lead data analysis efforts and collaborate across teams to drive technical strategy and research initiatives.