Head of AI

Role Overview

• Own AI strategy and research direction: Set the technical roadmap for our AI capabilities.
• Own agent quality and evaluation: Build and run the frameworks that tell us whether our investigation agents are improving.
• Build the breakthroughs yourself: Prototype a new technique in days, get it into the product, and measure the impact.
• Run fine-tuning and model experiments on real data: Own fine-tuning pipelines, context engineering, model migration, and cost/routing optimisation grounded in production data.
• Guide prioritisation across the AI team: You'll be the filter deciding which methods are actually worth a prototype this week.
• Lead a small team by doing: Set technical direction for the AI engineers, raise the bar through pairing and review.
• Partner with the CTO and engineering leadership: Turn the AI roadmap into shipped capability.
• Get in front of customers: Occasional direct customer exposure, translating what security teams need into concrete improvements to the ML pipeline.
• Set the pace: Ship prototypes in days, not quarters.

Requirements

• Hands-on technical leadership: A track record of leading AI work while personally building it.
• Shipped LLM/agentic systems to production: You've built and run generative-AI systems that real customers use, not research prototypes or slideware.
• Deep LLM-era technical depth: You can explain transformer architecture, training, fine-tuning (e.g. LoRA), and inference from first principles.
• Built evaluation frameworks for non-deterministic systems: You've designed and run evals for multi-step, non-deterministic agents: trajectory evaluation, LLM-as-judge, fine-tuning result measurement.
• Top-tier pedigree with a builder's edge: Experience at a leading AI organisation or strong AI-native startup where you raised the technical bar rather than coasted on the brand.
• Unambiguous startup signal: You've operated at early stage or built something from zero.
• Pace and urgency: You ship prototypes in days.
• Sharp, concise communication: You communicate clearly and tightly in a remote-first, English-speaking team, in writing and live.
• Nice to Haves: Security, vulnerability-management, or adversarial-domain background. Strongly preferred.
• Comfort in front of customers, able to translate agent behaviour and capability into terms a security team understands.
• Model cost/routing pragmatism: real experience cutting inference cost and migrating between models in production.
• Track record at a successful AI-first startup, scaling a system from experimentation to production impact.
• PhD or published work in ML/AI at top-tier venues, paired with real production experience.

Benefits

• Founding-level ownership and upside.
• Significant equity, a seat on engineering leadership, and a path to VP of AI as the team scales around what you build.
• Cybersecurity as a force for good.
• The work directly helps organisations stop attacks.
• Measurable impact, real customers, immediate feedback on what you ship.
• Build the AI-native company from the ground up.
• A well-funded Series A (Theory Ventures) with a Series B on the horizon, early enough that you'll set the technical standards for how AI investigates security at scale.
• A team you'll want to be measured against.
• Founders and engineers from Amazon, Elastic, and Tessian. Hands-on leaders who've been part of multiple acquisitions and an IPO.
• The hardest problem in the field, unsolved. Evaluating non-deterministic, multi-step agents against ground truth is an open problem, and we've built the exploit lab and 180+ tool agent infrastructure to attack it.