Applied Researcher

Requirements:

• Master’s, PhD, or equivalent experience in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, or related field with 4 or more years of relevant work experience
• Strong hands-on experience developing, evaluating, and improving machine learning models or foundation-model capabilities
• Solid understanding of machine learning, NLP, Large Language Models, neural architectures, embeddings, retrieval, ranking, personalization, or multimodal modeling
• Experience with model training, fine-tuning, adaptation, evaluation, benchmarking, and performance analysis
• Experience with feedback-driven learning systems, such as preference learning, reward modeling, bandits, reinforcement learning, or continuous model improvement loops
• Ability to design experiments, define meaningful metrics, interpret results, and communicate findings clearly
• Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar
• Experience working with large-scale datasets, model training or evaluation pipelines, and applied research workflows
• Ability to translate product or platform problems into concrete research questions and practical solution approaches
• Ability to consider tradeoffs across model quality, latency, cost, reliability, and maintainability

What you'll be doing:

• Research and evaluate machine learning and Generative AI capabilities for large-scale ecommerce applications
• Contribute to applied research in various areas such as LLMs, retrieval-augmented generation, embeddings, multimodal modeling, ranking, agentic systems, and feedback-driven learning
• Improve modeling approaches, training methods, adaptation techniques, and evaluation frameworks for foundation-model-powered ecommerce experiences
• Design and run experiments to measure model quality, robustness, efficiency, and customer impact
• Develop learning systems that improve from feedback signals, including reinforcement learning, preference optimization, reward modeling, bandits, and online/offline feedback loops
• Analyze model behavior, failure modes, and data patterns to identify limitations and propose practical improvements
• Build training, fine-tuning, evaluation, and experimentation pipelines for machine learning models
• Collaborate with senior applied researchers, machine learning engineers, and platform teams in advancing promising prototypes toward production
• Collaborate with engineering, product, and platform teams to integrate AI capabilities into eBay experiences
• Stay current with advances in machine learning, Generative AI, and foundation models, and evaluate relevant techniques through hands-on experimentation
• Contribute to scientific and technical standards through structured experimentation, peer review, documentation, and clear technical communication
• Contribute to publications, patents, internal research reviews, and technical discussions where appropriate

Nice to haves:

• Good collaboration and communication skills with experience working across research, engineering, product, or platform teams
• A strong sense of ownership and a track record of delivering meaningful applied research or machine learning contributions
• Experience contributing to applied research impact, such as publications, patents, shipped model improvements, productionalizing research prototypes, or measurable product outcomes

Perks and benefits:

• 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, veteran status, and disability, or other legally protected status
• Opportunity for accommodation if needed