US employers
Hire AI & LLM engineers for US product teams
Ganloss helps US hiring managers reach engineers who ship RAG, agents, and production ML—not résumé keyword noise. Search proof-first profiles, publish posts with explicit comp and timezone expectations, and keep applications organized as volume grows.
Built for full roles—not gig tasks
Ganloss is for US teams hiring AI product and delivery work with clear stacks. It is not a micro-task marketplace for annotation gigs or a generic board optimized for every industry.
2026 US salary benchmarks (indicative)
Bands vary by metro (SF/NYC vs remote-friendly), equity, and company stage. Use these ranges to set expectations in your job post—candidates self-select when comp is explicit.
| Role | Junior | Mid (3–5 yrs) | Senior / staff |
|---|---|---|---|
| LLM engineer | $120k–$150k | $160k–$200k | $200k–$260k+ |
| ML engineer | $110k–$140k | $150k–$190k | $190k–$240k+ |
| MLOps / platform | $115k–$145k | $155k–$195k | $195k–$250k+ |
Base salary only; equity and bonus vary widely. Remote roles may sit 10–20% below top-tier metros.
Timezone coverage
Many AI engineers on Ganloss work US-friendly hours from the US, Canada, Latin America, and Europe. State required overlap (e.g. “4+ hours US Eastern”) in your post to improve match quality and reduce async friction.
W2 vs contractor (1099) guidance
US applicants filter aggressively on employment type. W2 roles should state benefits, level, and on-site/remote policy. Contractor roles should clarify duration, hourly/day rate range, and whether the work is IC-compliant. Ambiguity here is a top reason strong candidates skip an apply click.
Collections
Intent-based talent hubs
Landing pages that open the directory with search presets—ML, junior ML, NLP/LLM, MLOps, and computer vision intent.
Collections
Intent-based job hubs
Landing pages with filters pre-applied on the public board—remote and hybrid workplace presets, ML and junior-ML searches, and NLP/LLM intent.
Why US AI hiring needs clearer signals
US teams compete on speed and comp transparency. When posts omit salary bands, timezone overlap, or W2 vs contractor status, you waste screens on mismatched applicants. Ganloss nudges both sides toward the same vocabulary: tools, outcomes, employment type, and proof of shipped work.
Search with stack intent
Filter profiles by role, tools, location, and proof signals—prioritize candidates who already describe eval harnesses, latency budgets, and production constraints.
Post roles US candidates trust
List skills, workplace type, timezone overlap, employment type (W2/1099), and optional comp bands next to a full description.
Review structured applications
Applicants arrive with profile context and optional CV material, tied to verified accounts for consistent employer review.
Export for ATS handoff
When recruiting volume grows, export applicant or inquiry data instead of copying fields manually.
A practical US employer workflow
- Align on comp & employment type — agree internally on W2 vs contractor, level, and salary band before writing the post.
- Publish on Ganloss — use structured fields plus timezone and stack clarity so candidates self-filter.
- Search proactively — browse talent search for passive candidates with niche LLM/ML combinations.
- Triage in one place — review applicants in your dashboard without losing profile context.
Related resources
- Hire LLM engineers — dedicated landing for RAG, agents, and LLM platform hires.
- Hire ML engineers — production ML, MLOps, and data platform roles.
- AI hiring guide — job posts, screening, and stack alignment.
- Global hire AI talent hub — broader international employer page.
US hiring teams — reported outcomes
Anonymized feedback from US employers using Ganloss. Comp bands, timezone overlap, and employment type in the post improved applicant quality.
Listing a salary band and US Eastern overlap upfront changed who applied—we went from résumé spam to three strong LLM finalists in the first week.
We needed a staff ML engineer with geospatial production experience. Stack-clear posts and profile proof lines let us skip candidates who only had notebook Kaggle projects.
For a six-month copilot integration we posted 1099 terms, hourly range, and tool stack explicitly. Contractors self-selected; we filled the role without LinkedIn noise.
Frequently asked questions
Profiles emphasize projects, tools, and skills—not only titles. Job posts mirror that structure so candidates self-select against your stack. That alignment reduces mismatched applications before the first screen.
List concrete skills and tools, workplace type (remote/hybrid/on-site), timezone overlap, employment type (W2 vs 1099), and a salary or hourly band when possible. Pair that with outcomes and production constraints so applicants know what “good” looks like.
Yes—many employers hire remote from Europe or Latin America with US-east US overlap. State visa/contract requirements and payment structure up front.
If the role is long-term product ownership with team integration, W2 is the clearer signal. Contractors fit bounded projects with explicit SOW—say so in the post to attract the right applicants.
Yes. Use public talent search with filters for role, location, tools, and profile proof signals, then encourage applications once your listing is live.
Use location and workplace fields on your post so US-based and US-timezone candidates can find roles that match. This page is optimized for US hiring managers; the global hub covers broader geography.
In major US tech markets, senior LLM engineers often land in roughly $200k–$260k base plus equity, with staff/platform roles higher. Remote-friendly companies may publish slightly lower bands—transparency still beats omitting comp.