Sr. Engineering Manager

Sr. Engineering Manager

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.

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 technical roadmap in alignment with business objectives
• Balance innovation (AI-first approaches) with long-term maintainability
• Manage technical debt strategically to ensure sustainable velocity and system health
• Guide architectural decisions that enable scale, reliability, and agility

What We're Looking For

Required Experience

• 5+ years of software engineering experience
• 3+ years of engineering management experience leading teams of 5–15+ engineers
• Proven track record of delivering high-quality, production-grade systems with measurable outcomes
• Experience defining and enforcing quality standards through automation and systems, not manual processes
• Experience partnering with Product Management to deliver customer-focused solutions

Our Tech Stack

• Languages: TypeScript, JavaScript, Python
• Frontend: Next.js, React
• Backend / Platform: Supabase (PostgreSQL, Auth, Edge Functions, Storage), Node/TypeScript services
• Data: PostgreSQL (Supabase + AWS RDS during migration), Redis
• Auth & Security: Supabase Auth, OAuth2/OIDC, GitHub, Trivy, Snyk
• Infrastructure: AWS, Docker, Kubernetes (for supporting services), modern CI/CD
• AI Tools: Cursor, Devin, GitHub Copilot, and modern agent frameworks where appropriate

AI-Native Mindset

• Hands-on experience with AI-powered developer tools and workflows (e.g., Cursor, Claude, Codex, or similar)
• Strong understanding of how to apply LLMs and agent-based systems to code generation, testing and validation, and developer productivity
• Ability to evaluate emerging AI technologies pragmatically and integrate them into real-world systems

Quality, Observability & Systems Thinking

• Deep understanding of modern testing strategies and quality engineering
• Experience building or scaling automated testing frameworks, CI/CD pipelines with enforced quality gates, and observability systems (metrics, logging, tracing, alerting)
• Experience defining and operating against SLOs/SLIs, reliability and performance targets, and data-driven engineering metrics
• Strong bias toward automation, instrumentation, and continuous validation

Leadership & Communication

• Strong coaching and mentoring skills
• Ability to drive alignment and influence across teams
• Clear communicator across technical and business contexts

Nice-to-Have (Strong Bonus)

• Software Security / Application Security
• Software Supply Chain Security (SCA, SBOMs, CI/CD security)
• Experience in cybersecurity, IoT, or embedded systems domains
• Experience in high-scale, high-reliability, or security-sensitive environments

What Success Looks Like

• Teams deliver consistently high-quality software with measurable improvements in reliability, defect rates, and customer satisfaction
• Automated and AI-driven testing systems provide broad, continuously improving coverage
• Quality issues are detected early—or prevented entirely—through intelligent, data-driven systems
• Engineering velocity increases without tradeoffs in quality, security, or stability
• Teams rely on systems, automation, and observability—not manual effort—to maintain excellence
• Engineering output is clearly tied to customer value and business impact

Compensation

Our salary ranges are based on experience and geographic location:

• $230,000 - $332,000

About Finite State

At Finite State, we're on a mission to secure the connected world. Our platform empowers product security teams to detect vulnerabilities, manage software supply chain risks, and ensure compliance across complex device ecosystems. From IoT to critical infrastructure, we provide unparalleled visibility into firmware and software components, helping organizations protect their products and customers.

We move with urgency and intent — we're transparent, own outcomes, put customers first, speak up, and learn fast — turning evidence into action. CLARITY is how we move fast without breaking trust.

• C - Customer first - Learn from customers. Ship with urgency.
• L - Leverage - Outsource the routine. Own the result.
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