Lead CV Applied Researcher

eBay-posted 2 days ago
Full-time • Mid Level
San Jose, CA
5,001-10,000 employees
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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 Core AI at eBay Core AI at eBay is dedicated to pushing the boundaries of artificial intelligence to enhance and optimize the marketplace experience. The team focuses on designing and implementing innovative AI solutions that transform how users interact with the platform, extract information from visual and multimodal data sources, and surface insights from complex market behavior. By harnessing the power of AI, Core AI helps drive business growth and empowers teams across eBay with the tools and intelligence to build smarter, more effective products. About the Role As an Sr. Applied Research technical lead at eBay, you will be at the center of how multi-modal intelligence is applied across products. You will not just support AI development, you will lead a team of applied researchers and machine learning engineers to help define how AI driven solutions operate, scale, and deliver value. Your work will directly influence model quality, operational efficiency, and the ways AI enhances user experience across eBay.

  • Team Development and Leadership: Build and lead a high-functioning team of applied researchers and machine learning engineers. Create a collaborative environment focused on delivery, accountability, and skill growth.
  • Research and Development Ownership and AI Model Lifecycle Management Own the creation, management, and evolution of multimodal solutions used across company’s AI initiatives. Design structured processes for data acquisition, model training, and inference. Design,develop and improve large scale multimodal retrieval systems. Maintain high levels of model quality through both offline and online evaluations. Partner with internal teams and external providers to ensure that the right AI solutions and features are available for eBay use cases.
  • Cross-functional Collaboration: Work with data scientists, engineers, and product managers to align AI solutions with eBay’s goals. Translate research and product needs into development plans and workflows.
  • Operational Strategy: Create work plans that reflect team priorities and long-term objectives. Adapt quickly to changing needs while keeping the team focused on outcomes that matter.
  • Process, Standards and Compliance Establish and maintain clear standards for how data is collected, and models are trained and evaluations. Work with responsible AI teams to ensure model safety.
  • Metrics and Reporting: Define key performance metrics for AI models. Maintain operational excellence standards for data pipelines and models in production
  • Ph.D. in AI or equivalent research experience.
  • Proficiency in Python and PyTorch for data processing, model training and inference.
  • Expertise in Vision-Language Models and image/video retrieval.
  • Deep understanding of key computer vision techniques for classification, image/video retrieval, 3D modeling, and Image/Video generation.
  • Two years plus of experience as a tech lead or a manager of science/engineering teams.
  • Comfortable coaching, and enabling team members to deliver high-quality work through clear direction, support, and collaboration.
  • Detail oriented in how you work with data, models, documentation, and process. Ability to catch inconsistencies early, ensuring standards are consistently met across workflows and deliverables.
  • Ability to clearly communicate with technical and non-technical teams, making complex issues easy to understand without oversimplifying.
  • Ability to break down and clarify ambiguous problems, identify what’s missing, and help teams move toward practical, well-defined solutions.
  • experience with frameworks for large-scale multi-GPU training (e.g. PyTorch Lightening, Nemo etc.) and for large scale inference (TensorRT, vLLM )
  • medical
  • financial
  • 401(k) eligibility
  • PTO
  • parental leave
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