Job description

Flam is building the next generation of interactive media through its content format. We are an AI-native technology company transforming how brands and consumers interact through immersive, interactive content. Our technology enables rich, app-less experiences that can be launched instantly on smartphones, creating a fundamentally different way for brands to engage consumers. We are backed by leading investors and already work with some of the world's largest brands. We are now building Flicks, our interactive media format for the US market.

The Role

As the Head of QA / Quality Engineering, you will own the overall quality strategy for Flam's products and technology platform. This is a hands-on leadership role that combines people leadership, engineering, automation, architecture, quality strategy, and operational excellence.You will work closely with Engineering, AI/ML, Product, DevOps, Security, and Customer Success to establish a world-class quality engineering organization capable of supporting rapid product development without compromising reliability —moving Flam from a primarily functional QA approach toward a modern, automation-first, engineering-led quality

organization.

The ideal candidate can build the function from the ground up, define the right processes and metrics, introduce the right

automation architecture, and personally drive the transformation.

1 Key Responsibilities

1. Build the Quality Engineering Function

Define and own Flam's overall QA and Quality Engineering strategy.

Establish the organization's quality vision, principles, processes, and engineering standards.

Build and scale the QA organization across manual testing, automation, performance, reliability, and specialized testing.

Define roles, responsibilities, career paths, and technical standards for the QA team.

Establish a culture where quality is everyone's responsibility, while QA provides the engineering framework, tooling, and

governance to achieve it.

Partner with engineering leadership to embed quality throughout the software development lifecycle.

2. Automation-First Testing

Build a comprehensive test automation strategy across frontend, backend, APIs, SDKs, and platform services, establishing

automation frameworks for:

UI Testing API Testing Integration Testing End-to-End Testing Regression Testing

Contract Testing Mobile / Web Testing

Significantly reduce dependency on manual regression testing.

Integrate automated testing deeply into CI/CD pipelines.

Establish appropriate test pyramids and testing standards across engineering teams.

Define automation coverage and quality gates for every major product area.

3. AI / ML Quality & Evaluation

Flam's products introduce testing challenges that go beyond traditional software QA. You will establish frameworks for

evaluating AI and multimodal systems, including LLM response quality and consistency, RAG accuracy and relevance,

hallucination detection and evaluation, retrieval quality, prompt and model regression testing, speech-to-text and text-to-

speech quality, voice interaction reliability, avatar and lip-sync quality, computer vision and multimodal interaction quality, AI

response latency, model/version regression, safety and undesirable model behavior, evaluation datasets and golden test

sets, and automated AI evaluation pipelines.

You will work closely with the AI engineering team to establish repeatable, measurable evaluation frameworks rather than

relying primarily on subjective testing.

4. Performance, Scalability & Reliability Testing

Own the quality strategy for systems operating at scale, including load testing, stress testing, soak/endurance testing,

scalability testing, API performance testing, concurrent-user testing, latency benchmarking, realtime interaction testing,

GPU/AI inference performance validation, failure and recovery testing, resilience testing, and capacity testing.

Establish clear SLA/SLO-oriented quality metrics and work with Engineering and DevOps to continuously improve

production reliability.

5. Release & Quality Governance

Establish a robust and predictable release process. You will define release quality gates, definition of done, regression

strategy, risk-based testing methodology, release readiness criteria, production and post-release validation, defect severity

and prioritization standards, escaped defect analysis, root-cause analysis, and release quality dashboards.

The objective is to enable faster releases with higher confidence, rather than adding process and slowing engineering

down.

6. Shift-Left Quality

Partner with engineering teams to introduce quality earlier in the development lifecycle. Drive adoption of unit testing

standards, integration testing, contract testing, API-first testing, developer-owned testing, static analysis, code quality

checks, automated regression suites, CI quality gates, and production observability-driven quality improvement.

QA should not be the final checkpoint before production; quality should be engineered into the product from the beginning.

7. Defect & Root Cause Management

Establish a data-driven approach to defect management, owning metrics such as defect escape rate, regression rate,

production defect rate, mean time to detect, mean time to resolve, defect recurrence, automation coverage, test execution

time, release confidence, and flaky test rate.

Drive systematic root-cause analysis for critical production issues and ensure corrective actions are implemented.

8. Quality Engineering Architecture & Tooling

Evaluate and establish the appropriate tooling and frameworks for Flam's technology ecosystem, including test automation

frameworks, API testing frameworks, performance testing platforms, CI/CD integration, test management and reporting, AI

evaluation frameworks, synthetic test data, test environments, service virtualization/mocking, and monitoring and

observability integration.

The Head of QA should be able to make pragmatic build-vs-buy decisions and create a scalable QA technology stack.

9. Cross-Functional Leadership

Work closely with Platform Engineering, AI/ML Engineering, Frontend Engineering, Product Management,

DevOps/Infrastructure, Security, Customer Success, and business teams — representing quality at the leadership level and

ensuring product velocity and product quality remain aligned.

What We Are Looking For

10+ years of experience in Software Testing / Quality Engineering.

Strongly Preferred

Experience testing AI/ML or Generative AI products.

Experience with LLM evaluation, RAG evaluation, or AItesting.

4+ years in a senior QA leadership role managing andscaling QA organizations.

Proven experience building or transforming a QA organization from manual QA toward automation and Quality Engineering.

Strong experience with test automation architecture.

Strong API, backend, frontend, and end-to-end testing experience.

Experience designing CI/CD-integrated automated testing.

Strong understanding of distributed systems and modern cloud applications.

Experience with performance and scalability testing.

Experience defining engineering quality metrics and release governance.

Strong programming/scripting skills (Python, Java, JavaScript/TypeScript, or similar).

Experience working closely with engineering and product leadership.

Experience with realtime systems.

Experience testing voice, video, computer vision, or multimodal applications.

Experience with high-scale consumer or enterprise SaaS platforms.

Experience with Kubernetes/cloud-native environments.

Experience building QA organizations in high-growth startups.

Experience with security, reliability, or compliance-oriented testing.

We are looking for a leader who:

Thinks like an engineer, not only a test manager.

Is comfortable going deep into architecture and technology.

Can write code and understand automation frameworks.

Can challenge engineering teams constructively.

Uses data rather than opinions to measure quality.

Is comfortable operating in a fast-moving startup environment.

Can balance speed, risk, and quality.

Builds systems and processes rather than becoming a bottleneck.

Can recruit, mentor, and develop high-performing QA engineers.

Has strong ownership and execution orientation.

Is comfortable dealing with ambiguity and building a function from the ground up

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