Head of AI

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Maze

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

🔒 Cybersecurity

🏢 Enterprise

Cybersecurity • AI • Enterprise

Maze is a cybersecurity company that leverages Agentic AI to manage and mitigate vulnerabilities within cloud environments. By automating the investigation and prioritization of vulnerabilities, Maze helps organizations identify critical weaknesses before they can be exploited by attackers. Their innovative approach significantly reduces the backlog of vulnerabilities, focusing on the most pressing security risks and enabling efficient remediation workflows.

📋 Description

• 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.