Senior AI Researcher

You will lead original research advancing core models that enable offensive-security capabilities, shaping experiments end-to-end and shipping results into production. You will collaborate closely with the VP of AI Engineering, the CEO, and a small AI engineering team to turn research outcomes into deployable capabilities. Work model: New York preference but open to remote; you must work EST hours.

Here’s How You’ll Make an Impact on the Team

Drive original research on offensive-security agents: reasoning, planning, tool use, and long-horizon autonomous operation

Advance the post-training pipeline, including supervised fine-tuning, RL from verifier signals, LoRA adaptation, and adversarial evaluation

Extend co-evolutionary self-training architecture with curriculum design, self-play dynamics, and reward modeling for security outcomes

Design and execute experiments end-to-end, from hypothesis through writeup

Build internal evaluation harnesses where no public benchmark exists and measure capability rigorously

Translate research into production handoffs: model cards, deployment notes, and documented failure modes

Contribute to external research outputs: papers, talks, responsible disclosures, and technical writing

Demonstrated original ML research output (published papers, widely cited preprints, significant OSS releases, or shipped research that materially advanced a production system)

Hands-on post-training experience with large language models (7B+ parameters) and end-to-end ownership of data, training, and evaluation pipelines

Direct experience with at least one of: RL from verifier/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 evaluations that measure real capability and avoid contamination or specification gaming

Strong Python and PyTorch skills, with experience in distributed multi-GPU training

Clear technical writing demonstrated by research memos, experiment writeups, or papers

Here’s What Else Might Help You Out

Working knowledge of offensive security fundamentals (trainable on the job)

Prior work on code-generating or code-reasoning models

Experience with sparse, delayed, or expensive reward signals in RL

Research in robustness, adversarial ML, or red-teaming of language models

Familiarity with long-horizon agent benchmarks (e.g., SWE-bench, Cybench, WebArena)

$150,000 - $220,000 a year - Full-time