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