What does a Senior LLM Engineer (RAG) do in this remote US role?

You design retrieval pipelines, eval harnesses, and production agents with latency and cost guardrails. Expect ownership of chunking, reranking, observability, and safe rollout—not demo-only notebooks.

How do I apply if I am outside the United States?

Read the listing’s location and work-authorization notes. Many US-remote roles require eligible time zones or contractor status—state your residency and availability clearly in the application.

Does Ganloss screen candidates before I interview?

Ganloss listings highlight required stack and proof of shipped LLM work. Employers still run their own interviews; use your profile and cover note to show RAG metrics, failure modes you fixed, and links to repos or write-ups.

Senior LLM Engineer RAG (Remote, US)

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À propos du poste

Ce que l’équipe cherche et comment le rôle s’inscrit dans l’organisation.

Northbeam Inference Labs is hiring a Senior Senior LLM Engineer — RAG & agents (contract-to-hire) on the Applied LLM team (Remote · United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Northbeam Inference Labs — Own retrieval, chunking, and evaluation for customer-facing copilots. Ship tool-calling workflows, latency budgets, and rollback-safe prompt releases.

Remote within US time zones; occasional on-site weeks in NYC.

Northbeam Inference Labs needs someone who can move LLM features, retrieval, agents, and evaluation from experiment to production—with tests, monitoring, and stakeholder-ready communication.

The Applied LLM group partners closely with product and domain experts; you will translate ambiguous requirements into measurable ML or LLM outcomes.

What you will do

Design and ship LLM features, retrieval, agents, and evaluation with offline evals, dashboards, and rollback plans.
Improve datasets, labeling, and metrics that correlate with user or business outcomes.
Review model changes for safety, bias, latency, and infrastructure cost before wide release.
Document decisions so on-call, support, and compliance teams can act without guesswork.
Collaborate async with written specs, demos, and recorded design reviews.

What we look for

Demonstrated production use of core skills such as Python, LangChain, OpenAI API, Vector DBs—with repos, writeups, or metrics we can verify.
Comfort operating the listed toolchain (Docker, AWS) in real environments—not tutorials only.
Ownership of at least one feature or model from design through post-launch review—including failure modes you handled.
Clear communication on trade-offs (quality, cost, latency, safety) with product and non-technical stakeholders.
Eligible to work in United States (or remote policy as stated in the listing).

Benefits & ways of working

Health, 401k, home-office stipend, LLM API budget.

Senior LLM Engineer — RAG & agents (contract-to-hire)

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