Jobs / United States / Adobe INC
Senior Product Manager, Data Platform - Knowledge & Retrieval
Adobe 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 1,036 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 221 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 Adobe INC paid sponsored hires in similar roles13 certified filings for “Product Manager” (Data Scientists) in NY: $140k–$170k, median $162k. Most were filed at wage level II (33%) — 2 lottery entries, ≈31% 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 — Adobe INC
The US Department of Labor certified 1,036 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 221 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 Adobe INC →
About the role
The Opportunity Adobe's data platform is a vertical data intelligence stack: raw signals are governed and routed at ingestion, enriched and catalogued into curated enterprise definitions, and ultimately transformed into retrieval-ready, agent-optimized knowledge assets. The knowledge tier sits at the top of that stack. Curated definitions become trustworthy, retrieval-ready knowledge. AI agents, self-service analytics, and enterprise decision systems consume this knowledge every day. As Senior Product Manager for this layer, you own its execution throughout the entire process. This includes how knowledge assets are modeled, published, maintained, and delivered to consuming agents via retrieval and integration interfaces. You'll turn a multi-quarter roadmap into shipped capabilities, working shoulder-to-shoulder with engineering, data science, and peer platform PMs. This is a builder's role at the sharp end of Adobe's agent-ready data strategy. What you'll own • Own the product roadmap — develop and drive a 6–12 month roadmap for knowledge-asset modeling, retrieval APIs, agent-integration interfaces, and embedding/RAG pipelines, prioritizing and re-prioritizing against shifting platform needs. • Lead delivery from inception to launch — provide project leadership and day-to-day management spanning engineering and build, making thoughtful quality / customer-value / time-to-market tradeoffs and mitigating risk. • Own knowledge freshness and lifecycle — define how curated definitions are ingested, versioned, deprecated, and kept current, so consuming agents never retrieve stale or orphaned knowledge. • Deliver the trust signals for the knowledge layer —freshness, lineage, and quality—so knowledge assets can be safely consumed by agents, while contributing these signals to the platform-wide agent readiness score. • Build human-in-the-loop correction workflows at the knowledge layer — define what triggers human review of a knowledge asset, and how corrections propagate to downstream consumers. • Write clear engineering PRDs — translate platform requirements into prioritized features, crisp specifications, and concrete performance measures and safety thresholds. • Partner across the platform team — align with peer PMs owning metadata, event telemetry, and cross-cutting governance so retrieval and embedding surfaces are built on governed, trustworthy foundations. • Prototype to think — use vibe-coding (Claude Code preferred) to stand up quick, testable prototypes that reduce risk in build choices ahead of engineering dedication. What you require to succeed • Track record: 7+ years of product management experience, including 2+ years owning a data platform, data infrastructure, or enterprise data product end to end — not just contributing to one. • AI knowledge-stack fluency: Working knowledge of knowledge-graph concepts, embedding pipelines, RAG architectures, MCP servers, and agent skills, plus a working understanding of how agents and models consume data and what makes an asset "agent-ready." • Quality workflows: Experience with human-in-the-loop quality and correction workflows in production data systems. • Specification skill: Ability to write engineering PRDs that translate complex technical systems into clear user problems, prioritized features, and measurable success metrics and guardrails. • Rapid prototyping: Ability to design and build quick prototypes through vibe-coding (Claude Code preferred) to de-risk decisions before engineering invests. • Systems thinking and business judgment: able to reason about the platform as a causal chain rather than independent features, and to articulate how a decision drives adoption, retention, and downstream platform value. • Cross-functional influence: Proven ability to drive