Product Designer

Maze

Summary of the Role: As an Engineer at Maze, you'll play a pivotal role in shaping our product, with significant focus on the development of AI Agents and ML systems. You will have the unique opportunity to design, build, and scale a product from the ground up, influencing everything from architecture decisions to ML integrations and the overall user experience. This is an exciting chance to be at the core of our technical team, driving innovation in AI-powered cybersecurity solutions and ensuring seamless integration across the stack. Your Contributions to Our Journey: * AI Agents: Play a pivotal role in the development and scaling of complex AI Agents to tackle some of the biggest challenges in cybersecurity. * Architect and Develop: Design and implement backend systems that effectively support agentic workloads, ensuring they are scalable, maintainable, and secure. * End-to-End Ownership: Take ownership of the entire development lifecycle, from concept and design to deployment and maintenance. * Integrate Seamlessly: Work closely with other engineers, our designer/product manager to ensure seamless integration of new features and services. * Optimize for Performance: Continuously monitor and improve application performance, security, and scalability. * Establish Best Practices: Define and enforce coding standards, best practices, and documentation to maintain high code quality. * Rapid Prototyping: Quickly prototype and iterate on new features, adapting to user feedback and changing requirements. * Mentor and Lead: As the team grows, mentor junior engineers and lead by example in technical discussions and code reviews. What You Need to Be Successful: * Extensive Experience: 7+ years of experience in backend development * Backend Mastery: Strong experience with backend development, including RESTful API design, database management, and server-side frameworks (e.g., Python). * ML/AI Understanding: Working knowledge of machine learning principles and experience integrating LLMs or other AI services into production applications. Familiarity with tools like LangChain, LlamaIndex, or similar frameworks is a plus. * Cloud Experience: Familiarity with cloud platforms (e.g., AWS) and their ML services, along with DevOps practices, including CI/CD and containerization (e.g., Docker, Kubernetes). * Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on delivering robust and scalable solutions. * AI Systems Architecture: Understanding of how to architect systems that effectively leverage AI capabilities while maintaining performance and reliability. * Collaborative Spirit: Excellent communication skills and the ability to work effectively in a cross-functional team. * Agility and Adaptability: Comfort working in a fast-paced startup environment with the ability to pivot and adapt as needed, particularly in the rapidly evolving AI landscape. Why Join Us: * Ambitious Challenges: We are using Generative AI (LLMs and Agents) to solve some of the most pressing challenges in cybersecurity today. You’ll be working at the cutting edge of this field, aiming to deliver significant breakthroughs for security teams. * Expert Team: We are a team of hands-on leaders with deep 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 becoming a challenge to most companies and helping them mitigate risk ultimately helps drive better outcomes for all of us.

Maze

Summary of the Role: As Product Engineer (Full Stack) at Maze, you'll be the technical force behind our customer-facing product experience, owning the complete stack from UI to API while moving at startup velocity. This is a unique opportunity to join as one of the early engineering team members of a well-funded startup building at the intersection of generative AI and cybersecurity. You'll work alongside our full stack product team with significant autonomy, owning your own API layer that powers the React application, enabling you to move fast and deliver exceptional experiences without dependencies or blockers. You'll take full ownership of feature development from concept to production, building both the elegant UI components that security teams interact with daily and the robust API infrastructure that makes it all work. Your success will be measured by delivery velocity, product quality, autonomous execution, and the customer experience you create—not by process adherence or ticket completion. This role is perfect for a hands-on full stack engineer who has thrived in fast-paced B2B SaaS environments, values independence and ownership, and wants to build something extraordinary with a modern tech stack from the ground up. Your Contributions to Our Journey: * Own Product Feature Development: Take complete ownership of features from front-end UI through to API implementation and database layer, delivering end-to-end functionality that delights customers and drives product adoption * Build with Modern Front-End Excellence: Craft exceptional user experiences using React, TypeScript, TailwindCSS, and the TanStack ecosystem (Router, Query, Table), creating intuitive interfaces for complex security workflows with attention to detail and performance * Design and Maintain API Infrastructure: Build and own performant REST APIs using our Python/FastAPI backend, ensuring scalable, maintainable services that power the front-end while maintaining clean architecture and performance optimization * Drive Autonomous Execution: Operate with high independence and startup energy—identify what needs building, make technical decisions quickly, and execute without waiting for perfect requirements or extensive process * Optimize for Performance and Scale: Continuously improve application performance across the stack, from database query optimization to front-end rendering efficiency, ensuring our platform scales elegantly as we grow * Contribute to Architectural Decisions: Participate in technical discussions and architectural planning, bringing your experience to help shape our front-end architecture and potentially grow into owning architectural decisions as the team scales * Balance Speed with Quality: Move at startup pace while maintaining high standards—ship features rapidly, gather feedback, iterate quickly, but never compromise on code quality or customer experience What You Need to Be Successful: * Proven Full Stack Product Experience: 6+ years building production applications with strong command of both front-end and back-end development—you must be genuinely skilled at both, not primarily one with dabbling in the other * Strong TypeScript and React Expertise: Deep, hands-on experience with TypeScript and React (or other modern component-based frameworks) in production environments, with strong understanding of component architecture, performance optimization, and modern patterns * Performant RESTful API Development: Proven track record building performant RESTful APIs with server-side frameworks, with experience designing scalable API architectures, optimizing endpoint performance, and building maintainable backend services * B2B SaaS Background: Direct experience in B2B SaaS product companies where you've built features for business users, understanding the unique requirements of enterprise product development and customer-facing applications * Database Performance Intuition: Strong understanding of database interactions and query optimization—you instinctively know when a query might blow up and how to structure data access for performance at scale * Startup Velocity Mindset: Thrives in fast-paced, ambiguous environments with bias for action—you "just go get it, go do it" without waiting for perfect specs, comfortable making pragmatic technical decisions quickly * Attention to Detail with Speed: Exceptional balance of moving fast while caring deeply about customer experience and code quality—you build things right the first time while maintaining velocity * Collaborative Independence: Comfortable working autonomously and owning your work end-to-end, while also collaborating effectively with designers, product managers, and other engineers when needed * Nice to Haves: * Python and FastAPI experience for our backend stack * Kubernetes/EKS experience for understanding our deployment environment and infrastructure * Temporal workflow orchestration experience for complex, distributed workflows * PostgreSQL optimization skills and experience with database performance tuning at scale * Experience integrating LLMs into applications, understanding how to build AI-powered features into production products * Experience with our specific front-end stack: TailwindCSS, TanStack ecosystem (Router, Query, Table), shadcn/ui, Radix UI * Infrastructure knowledge and comfort working with cloud platforms or infrastructure-as-code tools * Design system or component library development experience * Background at technical B2B SaaS companies where product velocity and customer experience were primary drivers Why Join Us: * Ambitious Challenge: We're using generative AI (LLMs and agents) to solve some of the most pressing challenges in cybersecurity today. You'll be building the product experiences that security teams interact with daily, creating intuitive interfaces for breakthrough AI-powered solutions. * Expert Team: We are a team of 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 - helping to stop cyber attacks ultimately helps deliver better outcomes for all of us. Your work will directly enable security teams to protect organizations worldwide from real threats. * 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 - you'll work with a modern, carefully chosen tech stack and help establish engineering practices that scale with us through hypergrowth. * Technical Ownership and Growth: True end-to-end ownership with your own API layer, freedom to make architectural decisions, and opportunity to grow into front-end architectural leadership as we scale the team and product.

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.