Engineering Manager, Automation Intelligence

Accountabilities:: Product Delivery & Launch Readiness: Own the engineering roadmap from beta through general availability and standalone commercial launch, partnering with product management on requirements, estimates, prioritization, delivery sequencing, and launch-readiness criteria. Service Reliability & Performance: Define and validate the service's operational limits, including throughput, latency, rate limiting, availability targets, and service-level objectives (SLOs), ensuring the platform meets production requirements. Detection Quality & Accuracy: Establish a continuous detection quality framework using representative test datasets, automated false-positive and false-negative testing, regression suites, and monitoring to maintain and improve detection performance. Automation Coverage & Classification: Guide detection capabilities for AI agents, assistants, stealth automation, headless browsers, and other emerging frameworks. Evolve classification systems to provide detailed context, confidence levels, identity verification status, and reason codes. Safe Deployment & Continuous Improvement: Manage staged rollouts of detection updates using feature flags, rollback procedures, and measurable release criteria. Incorporate customer feedback and reported detection errors into prioritized engineering improvements. Team Leadership & Development: Lead, coach, and develop a small team of senior engineers, manage recruitment as the team grows, and establish clear ownership, accountability, and opportunities for professional growth. Engineering Standards & Operational Excellence: Introduce effective engineering practices, including technical specifications, code review standards, repository ownership, and performance benchmarks covering delivery speed, throughput, predictability, failure rates, and recovery time. Cross-Team Collaboration: Establish effective working agreements with adjacent engineering teams, clarify shared responsibilities, and progressively reduce dependencies that prevent independent development and deployment. AI-Native Engineering Leadership: Lead an agent-first development workflow in which AI tools support specification, coding, review, and security checks, with appropriate human oversight for critical changes. Maintain and improve the tools, instructions, and review frameworks that enable this approach. Measurement & Knowledge Sharing: Evaluate the effectiveness of AI-assisted development against established engineering baselines, conduct regular performance reviews, and document lessons learned to inform broader engineering practices. API Design & Integration Standards: Ensure all product interfaces follow a contract-first approach, with OpenAPI specifications, automated breaking-change detection, generated documentation and SDKs, and APIs that are accessible to both developers and AI agents. Observability & Executive Communication: Own service observability through logs, metrics, traces, and SLOs. Communicate engineering progress, technical trade-offs, detection quality, and launch readiness clearly to leadership, product partners, and customer-facing teams. Requirements: Engineering Management Experience: At least three years of experience managing software engineers, ideally including senior and staff-level individual contributors, with a proven track record of delivering complete products through launch. Strong Technical Foundation: At least eight years of software engineering experience before or alongside management. Ability to critically assess technical designs and pull requests, engage in architectural discussions, and make sound technical decisions when trade-offs arise. Relevant Domain Expertise: Strong experience in at least one of the following areas: bot detection or management, fraud and abuse prevention, web security, browser or client-side engineering, or high-volume API services. Understanding of adversarial environments, where automated threats continually evolve, is particularly valuable. Quality-Driven Engineering: Experience building or operating measurement-driven engineering processes, including test datasets, precision and recall metrics, false-positive and false-negative tracking, regression testing, monitoring, and service-level objectives. Hands-On AI Coding Experience: Regular use of AI coding agents and a practical understanding of their strengths, limitations, and appropriate application in software development workflows. Product & Cross-Functional Leadership: Demonstrated ability to collaborate closely with product managers, researchers, and customer-facing teams, making dependencies, priorities, and technical trade-offs explicit. Communication & Decision-Making: Excellent written and verbal communication skills, with the ability to document decisions and their rationale and explain technical performance, detection quality, and release readiness to both technical and nontechnical stakeholders. Team Building & Autonomy: Ability to develop high-performing engineering teams, establish clear ownership structures, recruit effectively, and create an environment where experienced engineers can work independently and deliver consistently. Preferred Bot Detection & Security Experience: Direct experience with bot detection, bot management, anti-automation products, content delivery networks (CDNs), web application firewalls (WAFs), fraud prevention, or security platforms. Preferred AI Agent Knowledge: Familiarity with AI agent ecosystems, agent frameworks, verified-agent protocols, and how autonomous agents interact with websites and APIs. Preferred Browser & Automation Expertise: Understanding of browser internals, headless browsers, automation frameworks, and techniques used to detect or evade automated activity. Preferred API & Platform Experience: Experience operating API-first products with public OpenAPI specifications, software development kits (SDKs), and Model Context Protocol (MCP) or similar agent-facing interfaces. Preferred Organizational Experience: Experience establishing a new engineering team from an existing organization, separating codebases, defining ownership boundaries, and maintaining productive relationships between teams. Preferred Business Environment: Background in a high-growth SaaS company serving enterprise customers. Benefits: Fully Remote Work: Join a globally distributed, remote-first organization, subject to country eligibility and local employment requirements. Strategic Leadership Opportunity: Report directly to the VP of Engineering and lead a strategically important product initiative with clear objectives and significant decision-making autonomy. Product Ownership & Impact: Guide an emerging product from beta to commercial launch, with dedicated product management support and a clear path to market. Experienced Engineering Team: Lead an established group of senior engineers, with plans to expand the team and recruit additional specialized talent. Innovation & Technical Leadership: Help establish an AI-native engineering model, with the opportunity to influence development standards and practices across the wider engineering organization. Professional Growth: Develop expertise in AI agent detection, online fraud prevention, high-volume services, and advanced engineering management. Inclusive Work Environment: Join a workplace that values diverse perspectives, professional respect, and equal opportunity. Important Employment Considerations: The original posting states that certain countries are excluded for regulatory and security reasons and that visa sponsorship is not available. India-based eligibility, compensation, and contractual arrangements should be confirmed during recruitment.