Forward Deployed AI Engineer

Lunon•New York, NY
•$160,000 - $195,000•Remote

About The Position

Lunon is an AI-native consulting firm headquartered in New York City and backed by General Catalyst. The company embeds engineers inside businesses to understand operations and deliver software that can run independently. Its open-source research system ranks first on DeepResearch Bench. The Forward Deployed AI Engineer owns the model side of client engagements: selecting and adapting models to client data, evaluating their performance, and making them efficient and reliable for daily use. The engineer works alongside a Forward Deployed Data Engineer, who owns the data. The first engagement is helping an ERP company make its product AI-native. This is a fully remote, US-based role.

Requirements

  • Have fine-tuned or post-trained a model and be able to explain what worked and what did not.
  • Have built evaluations that influenced a decision.
  • Have shipped LLM features that real users depend on.
  • Have served a model in production and paid attention to cost and latency.
  • Use AI coding tools daily and evaluate results based on the system and its output, rather than the programming language.

Nice To Haves

  • Built on or integrated with an ERP, CRM, or accounting system.
  • Shipped extraction or search capabilities over messy business documents.
  • Worked in consulting, an agency, or a forward-deployed role, with experience becoming useful within another organization's environment quickly.

Responsibilities

  • Select the right model for each use case, including frontier APIs and open-weight models, and determine when fine-tuning is preferable to prompt improvements.
  • Fine-tune models when beneficial, using supervised fine-tuning, preference optimization, and parameter-efficient methods such as LoRA.
  • Build evaluation harnesses and benchmarks to determine whether changes improve performance.
  • Take models from training through serving and tune inference cost, latency, and quality.
  • Work with client teams to identify where AI can improve business outcomes and decline work that is unlikely to provide a worthwhile return.
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