Jobs / United States / Ebay INC
Senior Member of Technical Staff
Ebay INC · 🇺🇸 San Jose
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 478 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 314 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Ebay INC paid sponsored hires in similar roles14 certified filings for “Sr. Product Manager 2 - Technical” (Project Management Specialists) in CA: $163k–$217k, median $187k. Most were filed at wage level IV (64%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Ebay INC
The US Department of Labor certified 478 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 314 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Ebay INC →
About the role
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. Senior Technical Staff Member, Applied Research (T27) San Jose, CA Position Overview eBay is looking for a Senior Member of Technical Staff, Applied Research to update the search stack and develop new conversational search and natural language query features. This role involves hands-on work as an individual contributor who can shift between applied research, quick prototyping, production deployment, and close collaboration with engineering and product teams. This position centers on improving how users convey shopping intent through natural language and how eBay interprets, retrieves, ranks, and responds to those signals in modern search experiences. The ideal candidate blends solid research instincts with practical production skills and is eager to transform new NLP and LLM capabilities into scalable systems that deliver clear customer and business results. Based in San Jose, the role partners with applied research, search engineering, product, and platform teams to improve discovery foundations and foster richer conversational experiences. What You Will Own • Build, prototype, and productionize solutions for conversational search and natural language query understanding • Assist in updating the search stack across query understanding, retrieval, ranking, and response generation layers • Develop methods for intent understanding, query rewriting, semantic retrieval, result grounding, and multi-turn refinement • Partner with science and engineering teams to integrate data-informed approaches into production systems with strong latency, quality, and reliability characteristics • Build and evaluate NLQ solutions that help buyers express complex shopping needs more naturally and get more relevant outcomes • Build offline and online evaluation frameworks for conversational quality, retrieval efficiency, relevance, and customer impact • Contribute technical depth on model selection, timely and system build, experimentation, and measurement • Raise the technical bar through hands-on implementation, code reviews, build discussions, and cross-team collaboration What Makes This Role Distinct • This is a highly practical individual contributor role, not a people-management position • Success relies on technical implementation, prototyping speed, and the capacity to transform unclear problem areas into functional systems • The role combines applied research with product-focused engineering, placing a strong emphasis on delivering practical improvements in conversational search and NLQ. Minimum Qualifications • Bachelor’s degree in Computer Science, Engineering, or a related technical area, or equivalent hands-on experience • 8+ years of experience in applied research, machine learning, search, NLP, or related engineering roles • Strong hands-on experience building and shipping production-quality ML, NLP, or search systems • Deep expertise in one or more of the following areas: Dialogue-based search and information retrieval 1. NLP, intent understanding, semantic parsing, or query reformulation Large language model applications