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.

• 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

• 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-$220K/year

This role may fill quickly. Submit your resume to be considered.

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