Head of Generative AI

Octopus Energy

Introduction In just 10 years, Octopus Energy has evolved from a disruptive challenger into the UK’s largest energy retailer and a well-loved brand, serving more than 8 million customers. Our rapid expansion has been powered by consistently strong growth across diverse sales channels, the smooth integration of retailer acquisitions, and impressive customer retention.   We continue to lead and redefine the energy market through innovative time-of-use tariffs and data-driven commercial strategies that transform how customers engage with energy. We empower customers with dynamic pricing and "Intelligent" EV charging, rewarding them for shifting consumption to greener, cheaper periods. Role Summary You and a group of strong analysts will be generating insights to guide Octopus’ commercial decisions.  You will be leading the UK data commercial team and guiding global cooperation in this critical area. You will act as a strategic business partner to Commercial, Sales, Marketing and Energy Markets teams, combining hands-on technical expertise with strong team leadership.

Maze

SUMMARY OF THE ROLE: Maze is building an AI-native vulnerability management platform. Our autonomous agents investigate, triage, and remediate security findings the way a senior analyst would, only faster and at scale. As Head of AI, you'll own the intelligence that makes those agents work: the AI research and implementation strategy for the whole company, plus the crown-jewel technical problem underneath it. Our investigation agents run multi-step, non-deterministic trajectories across a toolset of 180+ tools, tested against a ground-truth exploit lab we built for exactly this purpose. Knowing whether they're getting better, and making them better, is the most important technical problem at Maze. It's the heart of this role. This is a hands-on leadership role, not a management layer. You'll set AI direction as a member of the engineering leadership team reporting to the CTO. But you'll spend most of your time building: designing evaluation frameworks for non-deterministic agents, running fine-tuning and model-routing experiments against real data, prototyping new techniques and getting them into the product. You'll lead a small, strong AI team (3–4 engineers today) by setting the technical bar and doing the work alongside them, while working closely with our ML tech lead and the product teams building agents day to day. Your impact comes from what you ship, not the size of your org. We're not looking for someone to run a large team from two layers up. We're looking for someone who wants to define how generative AI transforms cybersecurity and keep their hands on the code. This role suits a deep LLM-era practitioner who has shipped agentic systems to production, can reason about transformer internals and fine-tuning from first principles, and moves fast. We're a three-product company with a lot of surface area, a well-funded Series A (Theory Ventures) behind us, and a Series B on the horizon. The AI foundation you set now becomes the moat we compete on for years — this is a foundational hire whose standards will shape Maze's AI trajectory well past this raise. YOUR CONTRIBUTIONS TO OUR JOURNEY: * Own AI strategy and research direction: Set the technical roadmap for our AI capabilities. Stay ahead of the research curve to find, validate, and prioritise the techniques that differentiate Maze, and turn what's real into a concrete, sequenced roadmap while discarding the hype. * Own agent quality and evaluation: Build and run the frameworks that tell us whether our investigation agents are improving. That means trajectory evaluation, ground-truth scoring against the exploit lab, and end-to-end benchmarks for non-deterministic, multi-step behaviour. This is the core problem of the role. * Build the breakthroughs yourself: Prototype a new technique in days, get it into the product, and measure the impact. You'll spend most of your time hands-on in the codebase, acting as the technical product manager who guides it to production. * 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, not proofs of concept. * Guide prioritisation across the AI team: New techniques, papers, and ideas surface constantly. You'll be the filter deciding which of them are actually worth a prototype this week, and which are noise - keeping the team focused on what moves the needle. * Lead a small team by doing: Set technical direction for the AI engineers, raise the bar through pairing and review, hire as we scale, and stay close enough to the work to make the hard architectural calls yourself. * Partner with the CTO and engineering leadership: Turn the AI roadmap into shipped capability, and make sure evaluation is wired into how the whole team builds. * 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. Bring urgency to a domain where most of the field still moves slowly. WHAT YOU NEED TO BE SUCCESSFUL: * Hands-on technical leadership: A track record of leading AI work while personally building it. Strategy and implementation. You lead from the front. If you've moved permanently into management and stopped shipping, this isn't the right fit. * Shipped LLM/agentic systems to production: You've built and run generative-AI systems that real customers use, not research prototypes or slideware. You can point to agents or LLM features you put into production and improved over time. * Deep LLM-era technical depth: You can explain transformer architecture, training, fine-tuning (e.g. LoRA), and inference from first principles. We test this directly. A strong pre-LLM ML pedigree (RL, NLP, recommendations, ASR) is valuable but won't substitute for modern generative-AI depth. * 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. This capability is rare and it's the one we most want. It will set you apart. * 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. You move fast, own outcomes end-to-end, and don't need a large org around you to ship. Founder experience is a strong plus. * Pace and urgency: You ship prototypes in days. You make pragmatic calls on models, cost, and scope to keep momentum, and you're impatient with quarters-long cycles. * Sharp, concise communication: You communicate clearly and tightly in a remote-first, English-speaking team, in writing and live. You get to the point. * Nice to Haves: * Security, vulnerability-management, or adversarial-domain background. Strongly preferred. Every candidate we've rated highly has had it. Offensive security, vuln management, threat detection, or applying AI to security problems all count. * 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. YOUR FIRST 90 DAYS * Days 1–30: Get fully up to speed on every agent we've built and how our ML evaluation pipeline works today. Start drafting a short, mid, and long-term technical plan. * Days 31–60: Ship something fundamentally new — for example, fine-tune a small model and get it into production. * Days 61–90: Move onto bigger bets — RLHF for specific use cases, scalability of the evaluation approach, and deeper customer-facing model tuning.   THIS ROLE IS NOT FOR: * Manager-of-managers or 2nd/3rd-line leaders who direct rather than build. * Fractional, advisory, or part-time profiles. * Research-only backgrounds without production shipping experience. WHY JOIN US: * 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. You'd own it, at the intersection of generative AI (LLMs and agents) and cybersecurity. * 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. Most people who join do so because of how strong the team already is. * 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. * Cybersecurity as a force for good. The work directly helps organisations stop attacks. Measurable impact, real customers, immediate feedback on what you ship. * 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.

Maze

SUMMARY OF THE ROLE: As a Product Designer at Maze, you'll define what product design looks like for AI agents in cybersecurity — a problem space where the design patterns don't exist yet. This is an early-hire role at a well-funded Series A startup building at the intersection of generative AI and security, and you'll own the product surface end-to-end while working directly with our Head of Product, engineering, and founders. The product challenge is genuinely new. Our AI agents investigate vulnerabilities autonomously, surface findings with real context, and collaborate with security teams to cut through noise. Your job is to design how humans and agents work together — how trust gets built, how decisions get communicated, how complex security workflows become understandable. There's no playbook for this. You'll create one. This role suits someone who operates closer to a product director than a screen-shipper — driving direction, influencing strategy, and making business-impact calls, alongside the visual craft to deliver them. You'll be using AI tools daily to move faster, but your craft is the floor: every design choice should hold up on its own merits, independent of how it was generated. Success looks like a design system shipped and adopted across the product, and concrete product outcomes — feature adoption, POC-to-customer conversion, and customer satisfaction with the experiences you ship. YOUR CONTRIBUTION: * Define AI-Agent UX Patterns: Design how security teams collaborate with autonomous AI agents — how investigations get presented, how trust is established, how humans steer the agent without slowing it down. You're inventing patterns, not applying existing ones. * Drive Product Direction, Not Just Design: Operate as a product-led designer — shaping roadmap, challenging requirements, influencing leadership with research and business reasoning. The bar is "what should we build, and why" alongside "how should it look." * Own the Product Surface End-to-End: Take features from problem framing through shipped product, working in tight partnership with PMs and engineers — including backend. Run discovery, prototype rapidly, validate with customers, and stay close to implementation. No throwing designs over the wall, no shipping around the engineering team. * Build the Design System: Create and scale a component-based design system that becomes the foundation for everything we ship. Establish patterns for AI-human collaboration that the team can build on as we grow. * Pioneer AI-Assisted Design Workflows: Use AI tools to accelerate ideation, prototyping, and exploration — and develop the methodologies the rest of the team will adopt. Your daily workflow is itself a contribution. * Translate Research Into Design: Run customer research, talk to security teams directly, and turn what you learn into shipped product. Use AI-assisted analysis to move from insight to prototype in days, not weeks. * Set the Quality Bar: Establish what "good" looks like for product design at Maze across craft, consistency, and customer impact — as the foundation for a design team that scales behind you. WHAT YOU NEED TO BE SUCCESSFUL: * Visual Craft for Data-Dense UI: A portfolio that holds up to detailed scrutiny — strong colour theory, deliberate visual hierarchy, considered information architecture for complex workflows. This is the bar, not a nice-to-have. Your interview will dig into specific design choices and why you made them. * Product Design Track Record: 4-7 years of professional product design experience, with shipped work in B2B SaaS. We're flexible on years; demonstrated craft and clear ownership matter more than time served. * Data-Heavy B2B Interfaces: Direct experience designing for complex workflows, dense data, and technical audiences. Dashboards, investigation tools, developer products, security platforms — the kind of UI where information density and decision support actually matter. * AI as Tool, Not Crutch: You use AI tools (Figma AI, Cursor, v0, Midjourney, ChatGPT/Claude, etc.) as part of your daily workflow and can speak credibly to what they're good for and where they break down. Critically, every design choice you ship should hold up on its own — AI accelerates your work, it doesn't substitute for foundational craft. * Product-Strategic Thinking: Track record of driving product direction, not just executing on briefs. You've influenced what to build with research, framed problems for leadership, and made business-impact calls — ideally in environments where you operated without a dedicated PM. * Deep Engineering Collaboration: Demonstrable history of shipping work in tight partnership with engineers — including backend. Pairing on implementation, working in design tokens, understanding the systems your designs land in, building consensus rather than shipping around resistance. * Design Systems Experience: Background building or meaningfully contributing to a design system in a production product. You think in components, tokens, and patterns, not one-off screens. * Customer-Led Approach: Comfortable running discovery directly with users, synthesising what you hear, and using that to make calls about what to build. You don't wait for research to be handed to you. * Nice to haves: * Experience designing for cybersecurity, developer tools, or other technical product domains * Background designing agentic or AI-native product experiences (not just AI features bolted onto existing products) * Early-stage startup experience where you've operated without a manager or established design function WHY JOIN US: * Define a New Design Space: AI agents in security is a genuinely new problem. The interaction patterns, trust models, and workflows don't exist yet — you'll create them. The work you do here will influence how this entire category gets designed. * Fast-Track to Design Leadership: This is a founding-trajectory role and we're hiring for someone who wants a long-term home, not a stepping stone. As the team scales, the natural path is IC → Lead → Head of Design. If you have leadership ambitions, you'll have the runway to grow into them. If you'd rather stay deeply hands-on, the IC scope grows with the product. * Ambitious Challenge: We're using generative AI (LLMs and agents) to solve one of the most pressing problems in cybersecurity — the gap between vulnerability findings and meaningful action. You'll be designing at the cutting edge of this field. * Expert Team: Work alongside hands-on leaders with experience in Big Tech and Scale-ups. Our team has been part of the leadership teams behind multiple acquisitions and an IPO. * Impactful Work: Cybersecurity is a force for good. The experiences you design will directly affect how security teams protect organisations worldwide. * Build an AI-Native Company: We're building a new company in the AI era with the opportunity to design everything from the ground up — including how the design function itself works. You'll help pioneer AI-assisted design practices and set new standards.