Open-Weight LLM Tuning Specialist for Secure Deployments

Trenchant; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Trenchant is hiring an LLM Fine-Tuning Engineer to adapt open-weight language models for secure, local environments. You will own the post-training lifecycle including dataset design, supervised fine-tuning, and deployment support.

You will collaborate with cybersecurity teams to build high‑quality datasets, generate synthetic data, compare tuning methods (SFT, LoRA, QLoRA), and evaluate models for reliability beyond headline metrics.

Qualifications

• Strong experience fine-tuning LLMs in production or serious research.
• Practical experience with multiple tuning approaches: SFT, LoRA, QLoRA.
• Experience building and curating datasets for post-training.
• Experience generating and validating synthetic data for model training.
• Strong Python skills and GPU/memory optimization knowledge.

Responsibilities

• Fine-tune open-weight LLMs for secure/local use.
• Design, run, and compare post-training approaches.
• Build and improve high-quality training/evaluation datasets.
• Generate synthetic data to expand coverage and robustness.
• Assess model quality beyond headline metrics.
• Collaborate with engineers to deploy tuned models.

Skills

Fine-tuning LLMs

Tuning approaches

Python

GPU optimization

Debugging mindset

Tools

SFT

LoRA

QLoRA

Job description

Trenchant is hiring an LLM Fine-Tuning Engineer to adapt open-weight language models for secure, local environments. You will own the post-training lifecycle including dataset design, supervised fine-tuning, and deployment support.

You will collaborate with cybersecurity teams to build high‑quality datasets, generate synthetic data, compare tuning methods (SFT, LoRA, QLoRA), and evaluate models for reliability beyond headline metrics.