Applied Researcher 1 - Multimodal AI

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 role and the team: As an Applied Researcher 1 within the Computer Vision team, you will contribute to advancing eBay’s vision and multimodal capabilities for large-scale applications. You will work on training, fine-tuning, evaluation, and deployment of models across problem spaces including search, recommendations, content generation, and video understanding. This role is a hands-on researcher role where you will contribute across key stages of projects, apply rigorous methodology, and help bring high-quality ML solutions from idea to production. What you will accomplish: Develop and deploy computer vision or multimodal machine learning models for production applications across search, recommendations, content generation, and video. Contribute across key stages of research and applied ML projects, from problem framing and data preparation through experimentation, evaluation, implementation, and productionization. Build and improve training, inference, and evaluation pipelines for modern AI systems, including workflows that orchestrate multiple models and services. Apply strong experimental methodology and scientific rigor to model development, including metric design, offline and online evaluation, error analysis, and ablation studies. Contribute to core capabilities in classical and modern vision or multimodal problem spaces such as detection, segmentation, classification, contrastive learning, visual-language modeling, and multimodal retrieval. Build and improve agentic or VLM-powered pipelines that combine foundation models, retrieval, tools, and business logic to deliver scalable user and seller experiences. Partner with product managers, engineers, designers, and researchers to translate business opportunities into practical ML solutions. Communicate progress, technical trade-offs, and results clearly to cross-functional stakeholders.

Ph.D. or M.S. in Computer Science, Electrical Engineering, Machine Learning, Artificial Intelligence, Applied Mathematics, or a related technical field with a focus on computer vision, multimodal learning, or artificial intelligence. 1 or more years of experience building computer vision and/or multimodal systems for large-scale applications, ideally in one or more of the following areas: search, recommendations, video or live streaming. Strong understanding of classical computer vision and/or multimodal learning techniques, including detection, segmentation, classification, representation learning, and contrastive learning. Hands-on experience training, fine-tuning, evaluating, and deploying visual-language models, or other modern foundation-model-based systems, including familiarity with the broader modern AI stack used to develop, adapt, optimize and operationalize multimodal models. Practical experience with agentic AI workflows and/or VLM-powered pipelines in applied or production settings. Experience with rigorous experimentation, evaluation methodology, and data-driven model iteration. Proficiency in Python and modern ML/data frameworks such as PyTorch and TensorFlow, with fluency in agentic coding and AI-assisted development workflows. Strong written and verbal communication skills, with the ability to collaborate effectively across research, engineering,