QA Engineer – AI Native

Quick Facts

• Work Policy: Fully remote within Turkey
• Job Type: Full-time contractor
• Salary Range: Up to EUR 20,000 per year
• Experience Level: Mid-level

About Storyteller

Storyteller is a B2B SaaS platform that lets companies add Stories experiences to their apps and websites. It is a high-growth product used by major sports and media brands, and the team gets access to the latest hardware, AI tools, and product workflows.

About the Role

This is an AI-native quality engineering role. You’ll use AI as a core part of investigating software, building automated tests, understanding unfamiliar code, diagnosing failures, and making changes. The work covers SDK behavior and the integration between Storyteller’s SDKs, platform services, and APIs. It starts with iOS and Android, with later expansion to tvOS, Android TV, Fire TV, Web, and Roku. You’ll work as an individual contributor embedded with builders and product owners, not as a separate QA gatekeeper.

What You’ll Do

You’ll use AI coding agents and related tools to explore SDK codebases and speed up quality work, and identify the highest-value quality improvements based on product, technical, integration, and release risk. You’ll build and maintain unit, component, and integration tests for SDK behavior and service/API interaction, and put automated tests into CI and release workflows with clear, actionable failures. You’ll also verify that your tests catch meaningful breakage rather than just producing passing output.

When bugs appear, you’ll investigate them using code, logs, APIs, test results, and product behavior, and take well-understood defects through to bounded production fixes where appropriate. You’ll work with builders to improve testability and prevent repeated classes of defects, turn repeated testing or investigation work into reusable AI workflows, tools, or checks, and communicate evidence, impact, owner, and next actions in Slack and other team systems.

• Use AI coding agents and related tools to explore SDK codebases and accelerate quality work
• Identify the highest-value quality improvements using product, technical, integration, and release risk
• Build and maintain unit, component, and integration tests for SDK behavior and service/API interaction
• Put automated tests into CI and release workflows, with clear, actionable failures
• Verify that tests catch meaningful breakage rather than just producing passing output
• Investigate bugs using code, logs, APIs, test results, and product behavior
• Take well-understood defects through to bounded production fixes where appropriate
• Improve testability and prevent repeated classes of defects with builders
• Turn repeated testing or investigation work into reusable AI workflows, tools, or checks
• Communicate evidence, impact, owner, and next actions in Slack and other team systems

What You’ll Need

• AI-native execution: Uses AI for substantive technical work, gives useful context, inspects output, and redirects when needed
• Quality judgment: Decides what is worth testing, spots superficial tests, and considers false confidence, flakiness, and maintainability
• Agency and ownership: Investigates ambiguous risks with limited supervision and moves them toward outcomes
• Technical comfort: Works with repositories, APIs, CI, logs, CLI tools, and test frameworks, and can validate AI-generated code before landing it
• Systems thinking: Separates symptoms from root causes and builds reusable fixes for recurring problems
• Clear communication: Communicates concisely, with visible evidence, uncertainty, and next steps
• Learning velocity: Ramps up quickly on unfamiliar products and tools and changes approach when evidence says so

Swift or Kotlin expertise, traditional QA/SDET titles, and prior SDK platform experience are not required.

Nice to Have

• Mobile application or SDK experience
• Swift or Kotlin experience
• API integration experience
• Automated testing or test framework experience

Hiring Process

The process starts with a 20-30 minute conversation with the hiring manager, with no technical exercise. Next is a paid take-home task of about 60 minutes where AI tools are allowed, followed by a review of your submission and a 60-75 minute interview with the CTO.