[Hiring] QA Engineering Manager @Finite State

Role Description

We are seeking an experienced QA engineering leader to drive quality strategy, execution, and team leadership within an AI-first development organization. This is not a traditional QA management role — we are looking for a hands-on technical leader who can both architect modern quality systems and lead, mentor, and grow a high-performing QA engineering organization.

This is a true “player/coach” role: part technical authority, part people leader. You will be responsible for defining and implementing an AI-first quality engineering strategy while also managing and developing a team of QA engineers and automation specialists.

In our environment, AI is a first-class partner in software creation. The QA Staff/Lead Engineering Manager will define how quality is engineered into the product using automation, AI agents, scalable processes, and strong cross-functional collaboration with Product and Engineering.

If you have deep QA automation expertise, experience leading technical teams, and are excited about redefining quality in an AI-native development lifecycle, we want to talk to you.

What You’ll Do

• People Leadership & Team Development
  • Manage, mentor, and develop a team of QA engineers and automation specialists.
  • Provide regular coaching, feedback, performance management, and career development support.
  • Foster a culture of accountability, ownership, collaboration, and continuous improvement.
  • Partner with Engineering leadership on team planning, hiring, organizational growth, and resource allocation.
  • Help establish clear role expectations, growth paths, and technical standards for the QA organization.
  • Lead by example as a hands-on technical contributor while empowering engineers to grow and succeed.
  • Build an inclusive, high-performing engineering culture focused on learning and innovation.
• AI-Driven QA & Automation
  • Lead a team that is designing and implementing AI-agent-driven QA workflows (e.g., autonomous test generation, regression validation, autonomous testing agents).
  • Integrate LLM-based or AI-assisted tooling into CI/CD pipelines.
  • Leverage AI to improve test coverage, defect detection, root cause analysis, and release confidence.
  • Evaluate and introduce emerging AI QA tooling and frameworks.
  • Develop strategies for testing AI-based product features (e.g., model behavior validation, output consistency, guardrail enforcement).
  • Leverage AI to build autonomous QA systems that understand product context and proactively improve quality.
• Cross-Functional Quality Ownership
  • Partner closely with Product to drive outcomes based on acceptance criteria, quality gates, and risk assessments.
  • Work with Engineering leadership to embed quality earlier in the development lifecycle.
  • Drive a culture where developers co-own quality and automation.
  • Lead post-incident quality reviews and implement systemic improvements.
  • Define and standardize quality processes across squads.
• Technical Leadership & Quality Strategy
  • Own the end-to-end QA strategy for an AI-first engineering organization.
  • Lead the transition from traditional QA practices to AI-driven quality engineering.
  • Establish quality metrics, SLAs, and measurable standards across the product lifecycle.
  • Serve as a technical authority on testing architecture, tooling, automation strategy, and best practices.
  • Drive technical decision-making related to test automation, CI/CD quality gates, release confidence, and AI-assisted testing.
  • Balance strategic thinking with hands-on implementation and technical problem solving.
  • Communicate quality trends, risks, and strategic priorities to technical and non-technical stakeholders.
• Hands-On Execution
  • Build frameworks and reusable testing infrastructure.
  • Contribute directly to automation architecture, tooling, and implementation.
  • Mentor engineers on best practices in automation and AI-driven testing.
  • Participate in technical reviews, debugging, root cause analysis, and release readiness activities.
  • Remain close to the technology and development process while scaling the organization.

Qualifications

• 10+ years of experience in QA engineering, with deep expertise in automation.
• 3+ years of experience leading or managing technical QA or engineering teams.
• Demonstrated thought leadership and hands-on implementation experience building AI-driven or agentic quality systems.
• Proven experience designing and implementing automated test frameworks at scale.
• Strong programming skills (e.g., Python, JavaScript/TypeScript, Java, or similar).
• Experience with cloud-based CI/CD systems and modern DevOps practices.
• Demonstrated success leading cross-functional technical initiatives.
• Experience balancing strategic leadership responsibilities with hands-on technical execution.

Requirements

• Experience using AI tools and agents in development workflows.
• Deep understanding of AI agents, LLMs, and approaches for testing AI-driven systems.
• Experience with autonomous test generation or AI-assisted test maintenance is highly desirable.
• Understanding of challenges specific to testing AI systems (non-determinism, hallucination, evaluation frameworks).

Leadership & Influence

• Strong people leadership, coaching, and mentoring skills.
• Excellent communication skills with the ability to influence Product and Engineering leaders.
• Experience driving process improvements across teams and organizations.
• Ability to build trust, align teams, and lead through ambiguity and change.
• A mindset focused on systems thinking, scalability, continuous improvement, and operational excellence.
• Strong organizational and prioritization skills with the ability to manage competing demands.

What Success Looks Like

• QA is largely automated and AI-driven.
• Quality metrics are visible, measurable, and continuously improving.
• Developers rely on AI-driven test systems as part of daily workflows.
• Release confidence is high with reduced regression rates.
• Quality is embedded into the product lifecycle, not bolted on.
• The QA organization is highly engaged, continuously learning, and growing technically.
• Engineers receive clear mentorship, career guidance, and leadership support.
• Cross-functional teams view QA as a strategic engineering partner.

Why This Role Is Unique

You will help define what QA leadership looks like in an AI-first organization. This role goes beyond writing tests or managing a team — it is about building a modern quality engineering organization where AI agents collaborate with engineers to proactively identify, prevent, and fix defects.

This is an opportunity to shape both the technical direction of quality engineering and the growth of the people and teams responsible for delivering it.

Compensation

• Tier 1 (San Francisco, New York, Seattle): $210,000 - $240,000
• Tier 2 (All Other Locations): $190,000 - $220,000

The final base salary will be determined by experience, skill set, and specific location. In addition to base pay, this role is eligible for equity and benefits.

Company Description

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.

• 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.
• We are proud to be an Equal Employer Opportunity employer.