LLM Fine-Tuning Engineer (Open-Weight Models / Secure Environments)

We are hiring an LLM Fine-Tuning Engineer to help us adapt and evaluate open-weight language models for use in secure environments. This role is focused on the full post-training lifecycle: dataset design, supervised fine-tuning, parameter-efficient tuning, synthetic data generation, evaluation, and deployment support. You will work closely with technical teams operating in advanced cybersecurity contexts, helping turn foundation models into reliable task-specific systems. IMPORTANT NOTE: We are not looking for prompt engineers or “LLM whisperers.” We are looking for someone who understands post-training, data evaluation, and deployment deeply enough to make open-weight models reliable in real operational environments. WHAT YOU’LL DO - Fine-tune and adapt open-weight LLMs for specialized use cases in secure/local environments - Design, run, and compare different post-training approaches, including: - supervised fine-tuning (SFT) - parameter-efficient fine-tuning (LoRA / QLoRA and related methods) - preference tuning approaches such as DPO where appropriate - full fine-tuning when justified by the use case - Build and improve high-quality datasets for training and evaluation - Generate synthetic data and use it responsibly to expand coverage, improve robustness, and accelerate iteration - Assess model quality beyond headline metrics - Work with engineering teams to operationalize tuned models for local or restricted deployments - Collaborate with domain experts to translate real operational needs into measurable model requirements WHAT WE’RE LOOKING FOR - Strong experience fine-tuning LLMs or adjacent foundation models in production or serious research environments - Practical experience with multiple tuning approaches, ideally including SFT, LoRA, QLoRA - Experience building and curating datasets for post-training - Experience generating and validating synthetic data for model training - Strong Python skills and solid experience with: - PyTorch - Hugging Face Transformers - tokenization, data preprocessing and training pipelines - Good understanding of GPU constraints, memory/performance tradeoffs, quantization-aware workflows and practical training optimization - Strong debugging mindset and ability to investigate why a model improved, regressed, or failed NICE TO HAVE - Experience with secure, air-gapped, or otherwise restricted deployment environments - Experience deploying or serving tuned models - Background in cybersecurity, especially offensive security, vulnerability research - Experience with distributed training frameworks HOW WE THINK ABOUT THE ROLE This is not a “prompt engineer” role, and it is not limited to running a few LoRA jobs. We want someone who can think deeply about: - when fine-tuning is the right tool versus prompting or orchestration - how to create data that improves behavior instead of just inflating metrics - how to evaluate models in ways that reflect real operational value - how to make open-weight models reliable in constrained environments