Jobs / United States / Adobe INC
Core Agent Engineer
Adobe INC · 🇺🇸 4 Locations
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 roles348 certified filings for “Software Development Engineer” (Software Developers) in CA: $174k–$227k, median $193k. Most were filed at wage level II (46%) — 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 is reimagining the future of creativity — where generative AI, intelligent agents, and human imagination work hand in hand. As a Senior Full Stack Engineer, Agentic Product, you will be a key technical contributor for Adobe's up-and-coming AI-native experience that brings generative models, creative intelligence, and decades of differentiated Adobe capabilities together for hundreds of millions of creators. You will own hands-on buildout of the agent across the full stack and across Adobe's target platforms, from desktop and mobile to web and embedded surfaces. You will help reshape the way the team works in the age of agentic engineering. And you will be getting in on the ground floor to help shape the team and its practices. This is a rare opportunity to build the engineering foundations of Adobe's soon-to-be most visible AI product — f rom model integration and agentic reasoning patterns, to production infrastructure and cross-platform delivery. The right person brings deep, hands-on full-stack expertise, proven experience shipping agentic systems, a genuine enthusiasm for transforming how engineering itself is done with AI tools like Claude Code and Codex, and the craft to write production-quality code that holds up over time. What you'll do • Own full-stack implementation across Adobe's Agentic Product — from LLM integration and agentic reasoning layers, to API surfaces, client runtimes, and platform-specific delivery on desktop, mobile, and web. • Contribute to the production buildout of the agent: translating prototype-quality capabilities into robust, scalable, maintainable systems that meet Adobe's quality and reliability bar for a large, diverse audience. • Drive hands-on implementation of high-leverage components — including model orchestration, tool use, multi-step task execution, context and memory management, and agent evaluation infrastructure. • Champion the adoption of AI-assisted engineering practices — using tools like Claude Code, Codex, and emerging agentic development platforms to accelerate implementation, raise code quality, and fundamentally change how the team ships. Model new workflows where AI is a first-class collaborator in the development process. • Contribute to developer experience: tooling, local development loops, CI/CD ergonomics, and the scaffolding that lets a team move fast and confidently in a fast-moving AI product environment. • Stay current with the model capabilities ecosystem, evaluating new LLM approaches and frameworks, and contributing recommendations on when and how to adopt them. • Help build and maintain the evaluation and quality framework for agent behavior — the evals, benchmarks, and systematic feedback loops that allow the team to measure progress and catch regressions as capabilities evolve. • Partner with Engineering and Product leadership to translate product ambitions into technically grounded delivery — surfacing constraints early, proposing implementation options, and ensuring scope is set based on accurate technical understanding. • Collaborate across Adobe's platform, infrastructure, data, and security teams to ensure the Agentic Product is built on a foundation that supports guardrails, data protection, observability, and the operational requirements of a product at Adobe's scale. • Raise the bar for peers on the team by generating excellent code, driving high-signal code review processes , and sharing technical knowledge in agentic engineering. What you'll need to succeed • Solid, hands-on experience with large language models — including practical fluency with LLM APIs, prompt engineering, context window management, retrieval-augmented generation, and the operational realities of runni