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