Remote Forward-Deployed AI Engineer

Lunon

United States

Remote

USD 180.000 - 210.000

Vollzeit

Vor 5 Tagen
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

Lunon, an AI-native consulting firm based in New York City, is hiring engineers to walk in, understand operations, and ship. You’ll own the model side of engagements, choosing the right model, adapting it to client data, and delivering production-ready AI.

You’ll work with a Forward Deployed Data Engineer, fine-tune where it pays off (LoRA, supervision), build evaluation harnesses, and optimize inference cost, latency, and quality as you take models from training to serving.

Qualifikationen

  • You've fine-tuned or post-trained a model and can explain what worked and what didn't.
  • You've built evals that changed a decision.
  • You've shipped LLM features that real users depend on.
  • You've served a model in production and cared about its cost and latency.
  • You use AI coding tools every day and judge the result by the system and its output, not the language.

Aufgaben

  • Pick the right model for each job, from frontier APIs to open-weight models.
  • Fine-tune when it pays off: supervised fine-tuning, preference optimization, LoRA.
  • Build the evaluation harnesses and benchmarks that tell us whether a change made things better.
  • Take models from training to serving, and tune inference cost, latency, and quality.
  • Sit with the client's team, find where AI moves their numbers, and say no to work that won't pay back.

Kenntnisse

Fine-tuning models
Model evaluation
Production deployment
Cost & latency optimization
AI tooling

Tools

LoRA
Eval harnesses

Jobbeschreibung

Lunon, an AI-native consulting firm based in New York City, is hiring engineers to walk in, understand operations, and ship. You’ll own the model side of engagements, choosing the right model, adapting it to client data, and delivering production-ready AI.

You’ll work with a Forward Deployed Data Engineer, fine-tune where it pays off (LoRA, supervision), build evaluation harnesses, and optimize inference cost, latency, and quality as you take models from training to serving.

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