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