Jobs / United States / Salesforce INC
Software Engineering SMTS / LMTS - AI Core (REMOTE)
Salesforce INC · 🇺🇸 California - San Francisco
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 1,544 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 498 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 Salesforce INC paid sponsored hires in similar roles472 certified filings for “Software Engineering SMTS” (Software Developers) in CA: $193k–$246k, median $216k. Most were filed at wage level III (41%) — 3 lottery entries, ≈46% 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 — Salesforce INC
The US Department of Labor certified 1,544 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 498 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 Salesforce INC →
About the role
To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Engineering at Qualified Engineering at Qualified is a high-ownership, high-velocity environment where engineers ship customer-facing product to real enterprises at scale. We're building at the frontier of AI + product: developing agentic software and embedding AI directly into our platform, using modern toolchains and processes that help engineers move faster while staying focused on high-impact problem solving. Following our acquisition by Salesforce, we continue to operate with the pace and ownership of a startup, backed by the distribution, resources, and long-term runway of a global enterprise. Engineers have true end-to-end ownership from idea to production: scoping, implementation, testing, deployment, monitoring, and iterative improvement based on real customer feedback. You'll work alongside a high-talent-density team that cares deeply about craft, performance, reliability, and user experience, and that raises the bar through collaboration, code review, and strong engineering principles. You'll make architecture decisions, solve meaningful system design challenges, and see your work land quickly with customers, without layers of approvals slowing you down. The Role As an Engineer on the AI Core team, you'll operate as a technical leader who turns ambiguous, high-stakes AI problems into durable, high-quality systems, while raising the technical bar for the broader team. AI Core builds the retrieval systems and agent infrastructure that power how our AI agents find, trust, and use knowledge, and the developer tooling that lets engineers across the company ship production agents quickly and safely. You'll lead significant initiatives end to end: faithful answering over a customer's own knowledge, retrieval that combines RAG and hybrid search, content lifecycle controls such as freshness, deduplication and source precedence, transcript processing, and the frameworks, registries, and evaluation tooling that make building a production agent a paved path. You'll partner closely with product, design, and engineering leadership to decide what we build, how we build it, and how we measure whether answers actually got better. What You'll Do Lead retrieval initiatives end to end: Design, build, and operate retrieval systems using RAG and hybrid search (lexical plus vector, reranking, filtering) so agents answer from the right content with the right amount of context. Own delivery from scope and design through launch, monitoring, and iteration. Build developer tooling for production agents: Drive technical design for the frameworks and platform pieces our engineers use to build agents, including multi-agent orchestration, shared primitives for prompts, tools, tracing, and evaluation. Own answer quality: Diagnose why answers are thin, wrong, or inconsistent by separating retrieval problems from generation problems (prompt, token budget, model choice). Set a quality bar we can test against, and lead the