Principal Security Researcher – AI (Cybersecurity LLM Post-Training, Evals, and Environments)

What you'll do

• Build cybersecurity training and evaluation environments, datasets, graders, and benchmarks with verifiable outcomes.
• Develop and evaluate LLM post-training methods and iterative model-improvement workflows; analyze failures and regressions.
• Collaborate across security research, ML, infrastructure, and product teams to bring research capabilities into production.

What they ask for

• Hands-on experience in one or more cybersecurity areas.
• Practical experience developing or evaluating LLM-based systems for cybersecurity, source code, software engineering, or tool-using agents.
• Experience designing datasets, benchmarks, graders, evaluation frameworks, or tasks with verifiable outcomes; and experience with at least one post-training or model-adaptation method.
• Strong programming skills in Python and at least one of C, C++, Rust, Java, or Go; experience with large codebases, security tools, testing frameworks, build systems, debuggers, and containerized environments.

Nice to have

• Experience building reinforcement-learning environments, cybersecurity or coding agents, or other tool-using AI systems.
• Experience with DPO, RLHF, RLAIF, reinforcement learning, process supervision, model distillation, or verifiable cybersecurity tasks.

Pay

$163,200.00 - $264,000.00/yr (base salary)

Tools

Python, C, C++, Rust, Java, Go

AI focus

LLM and autonomous-agent cybersecurity research, including dataset and environment development, evaluation, and post-training methods such as supervised fine-tuning, preference optimization, reinforcement learning, reward modeling, rejection sampling, and distillation.