Director of Engineering, Webapp & AI Attack

• Get to Know Us
• Lead and develop the Webapp and AI Attack engineering teams, setting clear ownership, goals, and expectations across the portfolio.
• Define a coherent strategy and roadmap that connect customer needs, product priorities, offensive research, and durable engineering investments.
• Build an effective leadership structure as the organization grows; hire, coach, and develop engineering managers, technical leaders, and senior individual contributors.
• Set operating mechanisms that make outcomes, progress, capacity, quality, dependencies, and risks visible without adding unnecessary process.
• Own the health and performance of the organization, including talent planning, career development, timely feedback, accountability, and succession planning.
• Foster an inclusive, psychologically safe environment where teams can raise risks, learn from failures, and do their best work.
• Ensure the platform can reliably map pages, forms, APIs, parameters, and authentication surfaces across modern applications—including JavaScript-heavy single-page applications and complex user flows.
• Partner with technical leaders to improve attack coverage, framework breadth, and the quality of the application context available to downstream testing.
• Establish benchmarks, regression practices, and production signals that reveal coverage gaps and prevent quality from silently degrading as applications and frameworks evolve.
• Use customer feedback, production behavior, and missed opportunities to prioritize improvements and close the loop between discovery, testing, and customer outcomes.
• Set the strategy for turning AI/LLM and agentic-system security research into safe, reliable, repeatable attack capabilities in NodeZero—not one-off demonstrations or manual-only workflows.
• Guide teams in assessing AI applications and infrastructure, including trust boundaries, permissions, tool use, data sources, model-serving and retrieval architectures, and enterprise deployment patterns.
• Help teams evaluate and productize relevant attack classes, such as prompt injection, unsafe tool use, and AI supply-chain or deployment risks, with appropriate safeguards and clear customer value.
• Establish quality standards for testing, evaluation, reliability, observability, cost management, and operational readiness in AI-powered offensive workflows.
• Maintain a strong connection to the evolving AI security landscape and help the organization distinguish durable customer needs from short-lived trends.
• Partner with Product and leaders across Engineering, Platform, and customer-facing functions to translate ambiguous opportunities into sequenced plans with explicit outcomes and ownership.
• Make and communicate tradeoffs across customer impact, speed, coverage, reliability, safety, cost, and long-term maintainability.
• Drive complex, cross-team initiatives from planning through production, identifying dependencies and risks early and aligning stakeholders on decisions and changes.
• Maintain sufficient technical depth to evaluate architecture, system health, and engineering quality; partner with senior ICs rather than becoming a day-to-day decision bottleneck.
• Ensure strong engineering practices across design, implementation, testing, release, monitoring, incident response, and learning from production issues.
• Use customer feedback and product and engineering signals to assess results and continuously improve how the organization delivers.
• Significant experience leading software engineering teams that build customer-facing products, ideally in cybersecurity, security testing, or another technically complex SaaS domain.
• Experience leading multiple teams and developing managers and senior technical leaders, with a track record of building accountable, high-performing organizations.

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