• Choose the right model for each problem, from frontier APIs to open-weight models, and know when fine-tuning outperforms a better prompt
• Fine-tune where it pays off: supervised fine-tuning, preference optimization, and parameter-efficient methods such as LoRA
• Design the evaluations and benchmarks that show whether a change made things better
• Take models from training to serving, balancing cost, latency, and quality
• Work with the company's team to find where AI will change their results most, and decline work that won't repay the effort
• YOU MAY BE A FIT IF
• We weigh what you have shipped and sustained over years of experience.
• You have fine-tuned or post-trained a model and can explain what worked and what didn't
• You have built evaluations that changed a decision
• You have shipped LLM features that people depend on
• You have served a model in production and owned its cost and latency
• You work with AI coding tools every day and judge the result by the system it produces
• STRONG CANDIDATES MAY ALSO
• Have built on or integrated with an ERP, CRM, or accounting system
• Have shipped extraction or search over complex business documents
• Have done consulting or forward-deployed work, and know how to earn trust inside a company that isn't yours
• Remote within the US. Lunon is headquartered in New York City.
• 1. A 30-minute conversation with the team about what you've built and what we're building.
• 2. A technical deep-dive into one system you owned, from the business problem to production.
• 3. A working session with the team on a real client problem.
• 4. References, then an offer. The process takes about two weeks.
• Beware of recruitment fraud. All official correspondence comes from a lunon.ai http://lunon.ai email address. We will never ask you to share bank account information, cash a check, or purchase equipment as part of the hiring process.