Senior AI Researcher

We're hiring our first dedicated AI Researcher to advance the core models powering Ares. You'll work alongside our VP of AI Engineering and a small AI engineering team, with direct collaboration with our CEO — a researcher and practitioner with 26 years of offensive security experience, contributions to the OWASP API Security Top 10, and a permanent exhibit at The Mob Museum.

This is a research role, not an applied ML role. You'll own original research on offensive security agents — how they reason, plan, use tools, and operate autonomously over long horizons. You'll design experiments end-to-end, build the evaluation infrastructure the field doesn't yet have, and translate research wins into capability that ships.

The feedback loop is fast and adversarial. Research that proves out goes into production. Research that doesn't gets killed quickly so the next bet can start.

Core Experience That Matters Most

• Original ML research output — published papers, widely cited preprints, significant open- source releases, or shipped research that materially advanced a production system
• Hands-on post-training experience with language models at the 7B+ parameter scale, end-to-end ownership of a pipeline including data, training, and evaluation
• Direct work with at least one of: RL from verifier or reward signals, preference optimization (DPO/IPO/KTO), or supervised fine-tuning with synthetic data pipelines
• Experience with agentic LLM systems — tool use, multi-step reasoning, planning, or long-horizon execution
• Ability to design evaluation that measures real capability and avoids contamination or specification gaming
• Strong Python and PyTorch, with experience in distributed training at multi-GPU scale
• Clear technical writing — research memos, experiment writeups, papers, or equivalent
• Working knowledge of offensive security fundamentals (we'll teach you the rest if you bring strong ML depth)
• Prior work on code-generating or code-reasoning models
• Experience with sparse, delayed, or expensive reward signals in RL
• Research on robustness, adversarial ML, or red-teaming of language models
• Familiarity with long-horizon agent benchmarks (SWE-bench, Cybench, WebArena, or similar)