Senior AI Researcher - New York, NY

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
• Collaborate with engineering teammates on research methodology and experimental design

• 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)