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