Sr. Engineering Manager

Finite State partners with product security teams, the guardians of our connected world, to create transparency for their connected devices and supply chains. Our platform handles connected devices and embedded systems across all industries, including those found in enterprises, healthcare, utilities, connected vehicles, manufacturing facilities, critical infrastructure, and government entities. We are a fast-growing series-B company with a fully distributed workforce. Led by a team of seasoned experts, we are a mission-driven team passionate about arming our customers with the actionable insights, critical vulnerability data, and remediation guidance necessary to mitigate product risk and protect the connected attack surface. We are committed to a remote first culture. Sr. Engineering Manager (AI-Native) United States or Canada · Engineering · Full-time Overview We are seeking an Engineering Manager to lead and grow high-performing teams while redefining how modern, AI-native engineering organizations build and ship software. This role is for a leader who has managed teams of 5–15+ engineers and is passionate about building systems where quality is enforced, measured, and continuously improved through automation, observability, and AI-driven workflows. You will be responsible for driving execution, scaling teams, and embedding AI-powered development and testing practices into every stage of the SDLC. Delivering consistently high-quality, production-grade software is a key requirement of this role. What You'll Do Team Leadership & Execution Lead, mentor, and grow a team (or teams) of 5–15+ engineers Drive delivery of software that meets strict, measurable standards for quality, reliability, and maintainability Establish clear expectations where quality is owned by the team and enforced through systems, not heroics Foster a culture of accountability, continuous improvement, and engineering excellence Customer Impact & Product Excellence Ensure engineering decisions are grounded in customer outcomes and product impact Partner closely with Product Management to translate customer needs into scalable, high-quality systems Define and track metrics connecting engineering output to customer satisfaction, product adoption, and business outcomes Balance speed, quality, and innovation in service of real-world user value AI-Native Quality & Testing Systems Define and implement AI-driven quality strategies across your teams Build and operationalize automated and autonomous testing systems, including AI-generated test cases (unit, integration, end-to-end), self-healing test suites, and agent-assisted validation Leverage LLMs and agent-based systems to continuously expand test coverage, identify edge cases, and reduce manual QA effort while increasing confidence Ensure quality is continuously validated in CI/CD, not deferred to later stages Process, Tooling & Observability Design and enforce engineering processes where quality gates are automated and non-bypassable Implement AI-powered tooling across the SDLC: code generation and review assistants, automated code quality and security analysis, and intelligent CI/CD pipelines with adaptive testing Establish comprehensive observability including logging, metrics, tracing, alerting, and SLOs/SLIs aligned with customer expectations Use production data to detect issues early, predict and prevent failures, and drive continuous evidence-based improvement Track and improve key engineering metrics: test coverage, mutation testing scores, defect rates, production incident frequency, and service reliability AI-Native Engineering Practices Define and implement AI-first development workflows across your teams Evaluate and integrate modern AI tooling (copilots, LLMs, agent-based systems) Ensure AI adoption increases both velocity and quality Stay current with emerging AI capabilities and translate them into practical engineering improvements Technical Strategy & Execution Contribute to and execute the te