Maze

• Build Pipeline From Scratch: Create and execute outbound prospecting strategies using a mix of cold calling, LinkedIn, events, and network leveraging to generate qualified opportunities with cloud-focused tech companies and financial services organisations. • Own the Full Sales Cycle: Drive every stage from first touch through contract negotiation and close, navigating complex enterprise buying processes with multiple stakeholders and decision-makers. Work collaboratively with your Security Consultant on technical validation while maintaining control of the commercial relationship. • Lead Consultative Discovery: Run discovery conversations that uncover real pain around false positives, alert fatigue, limited security resources, and the gap between vulnerability findings and actionable remediation. Understand the customer's world before proposing solutions. • Get Hands-On With the Product: Develop deep product knowledge to conduct meaningful first conversations, understand how our AI agents investigate and triage vulnerabilities, and speak credibly about technical workflows. Our strongest AEs can demo the product themselves. • Pioneer Our Go-to-Market: Contribute directly to sales methodologies, playbooks, and best practices as we build the GTM function. Provide market feedback that shapes product roadmap and positioning. Refine our ideal customer profile and target segments based on what you learn in the field. • Collaborate With Engineering and Product: Work closely with technical teams to communicate customer needs, participate in feedback loops, and bring a data-driven perspective to how we prioritise and position capabilities.

• 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.

• Make Our Products Brilliant: Feed research directly into product and engineering — work close to the roadmap and the codebase to sharpen how we detect, prioritise, and remediate, building capabilities that outclass the competition • Shape How Our AI Understands Risk: Translate deep threat research into the labels, signals, and product feedback that train our models to prioritise vulnerabilities like a seasoned researcher • Lead Our Security Research Function: Set the direction, standards, and methodologies for how Maze researches, validates, and prioritises cloud and application security threats, scaling a small team of researchers as we grow • Find Novel Vulnerabilities That Get Reach: Surface original research and build narratives — blog posts, technical talks, podcasts, video, conference presentations — that earn real reach and give Maze technical credibility with the security community • Build Authoritative Technical Intelligence: Produce detailed research on exploitation techniques, attack vectors, and remediation across cloud infrastructure and application security, enriched with CVE, advisory, and threat-intel sources • Set the Standard for Research Quality: Establish the frameworks and review processes that keep our vulnerability assessment consistent, defensible, and ahead of the threat landscape • Grow the Bench: Mentor and develop researchers, raising the technical bar of the team and creating a research culture others want to join

• Build Production-Grade Evaluation Systems: Design and implement comprehensive evaluation frameworks that measure agent performance, track improvements over time, and ensure our AI systems deliver consistent value to customers • Drive Experimentation-to-Production Pipeline: Own the entire ML lifecycle from prototype to production, building scalable systems that enable rapid iteration while maintaining reliability and performance in customer environments • Enable Cross-Team ML Integration: Work closely with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value and product differentiation • Optimize AI Agent Performance: Continuously improve our AI agents through systematic experimentation, prompt engineering, and architectural enhancements, measuring success through customer impact and system performance • Scale ML Infrastructure: Build the foundational ML systems, monitoring, and tooling that will support our growth from startup to scale, ensuring we can deploy new capabilities quickly without compromising quality • Partner with Engineering Leadership: Collaborate directly with our CTO through regular check-ins and strategic alignment while operating with high autonomy and self-direction in day-to-day execution • Mentor Through Excellence: Provide natural mentorship to junior ML engineers through code reviews, technical guidance, and sharing practical experience from building production ML systems

• Harden Our Cloud Infrastructure • Own Application Security • Build Security Tooling and Monitoring • Run Compliance Pragmatically • Establish Security Policies That Enable • Automate Security Operations • Manage Vendor and Supply-Chain Security • Enable Incident Response

• 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.

• Build the Channel From Scratch: Design and execute the channel strategy end-to-end -- partner tiers, economics, enablement, deal registration, and reporting. Bring your existing reseller and VAR relationships in cybersecurity to bear from week one, and use them to generate qualified pipeline alongside our AE team. • Activate the Cloud Marketplaces: Get Azure and GCP partnerships moving and accelerate the work already underway with AWS. Own the relationships with cloud field teams, navigate marketplace listings and co-sell motions, and turn passive listings into active pipeline. • Test New Partnership Vectors: Lean into emerging partnership models we've started to prove out, like investor and PE portfolio referral programs. You'll be expected to think from first principles about where Maze's next channel of growth comes from -- not just replay the standard channel playbook. • Own Tech Partnerships Pragmatically: Manage our technical partnerships portfolio, prioritising the ones that drive commercial outcomes through co-marketing, co-selling, or joint solutions. Avoid the trap of low-value integration work that doesn't move the needle. • Work in Sync With Sales: Keep a tight working rhythm with our AEs and sales leadership so partner motions show up in deals, not as a separate track. Make sure channel and direct work amplify each other rather than collide. • Bring the Partner Voice Into the Company: Feed market signal back into product, marketing, and leadership -- what partners need to sell us, where co-marketing lands, what competitive positioning shows up in the field.

• Own US Outbound from Day One • Pick Up the Phone and Win the Conversation • Engineer Your Own Stack • Test, Measure, Pivot Fast • Become Known in the Security Community • Partner Tightly with the AE • Feed the Marketing Engine • Earn Your Path to AE

• Build the operational foundation for scale • Set the rhythm of the company • Drive operational leverage through AI • Turn institutional knowledge into infrastructure • Drive financial clarity for the business • Build a proactive compliance and people ops function

• Own engineering leadership for a growing portion of our org: Take clear accountability for a set of small, high-output product teams (typically 3–5 engineers each), with tech leads reporting directly to you. Ensure every team has unambiguous priorities, strong support, and everything they need to move fast. • Stay close to the technical work: Engage directly with architecture decisions, code reviews, and technical discussions across your teams. We expect you to spend meaningful time understanding what each team is building — not as a gatekeeper, but as a trusted technical voice who can contribute, challenge, and improve. • Grow the engineering leaders of Maze's future: Take ownership of career development, performance management, and coaching for tech leads and senior engineers in your teams. Build the kind of trust with engineers that comes from genuinely knowing their work, their growth areas, and their ambitions — not from generic 1:1s. • Drive cross-team coordination without creating bureaucracy: Own the planning and coordination layer that keeps multiple small teams aligned and unblocked. Keep it lightweight, decision-focused, and in service of engineering velocity — not process for its own sake. • Lead technical hiring: Take ownership of engineering hiring within your area, from defining the bar and shaping interview processes to closing exceptional candidates. The quality of who we hire is one of the highest-leverage things either of us can do. • Build the org we need to scale: Work closely with Santiago to design team structures, identify emerging leaders from within, and evolve how we operate as the team doubles. We grow leaders from the inside where we can — you'll be central to identifying, developing, and empowering the next generation. • Maintain technical excellence as we grow: Partner with tech leads to uphold code quality, shared engineering practices, and high standards across the org — without letting process replace judgment.