QA Engineer - AI Native

🇩🇿 Up to EUR 20,000 per year, on a full-time, contractor contract 
🌎 Fully remote working from anywhere in Algeria!   
✨ Exciting high growth product, relied on by leading global brands, particularly within sports   
💻 Working with the latest hardware, AI tools, and product workflows.  

ABOUT US 

Storyteller is a high-growth B2B SaaS platform that enables companies to add Stories experiences to their own apps and websites. Stories help our clients increase engagement, retention and revenue. 

Our platform combines SDKs, publishing tools, analytics and advertising support so enterprise customers can launch a complete Stories experience quickly. We work with major sports and media brands, and the experiences we power are used by large audiences around the world. 
 

About the Role 

This is an AI-native quality engineering role. You will use AI as a core part of how you investigate software, build automated tests, understand unfamiliar code, diagnose failures and make changes. 

Your work will span the quality of SDK behaviour and the integration between Storyteller SDKs, platform services and APIs. 

Storyteller has SDKs across iOS, Android, Web, tvOS, Fire TV, Android TV and Roku. The role will initially focus on iOS and Android, with opportunities to contribute across the wider SDK portfolio over time. 

This is a hands-on engineering role, but not a separate QA gatekeeping function: 

• You’ll use AI coding agents and related tools to explore the SDK codebases, understand behaviour and accelerate quality work. 
• You’ll care about tests that protect important behaviour and run in real development and release workflows. 
• You’ll use product, technical, integration and release risk to identify the highest-value quality improvements. 

You will work as a senior individual contributor inside the Storyteller team. Quality belongs to the whole team, and you will work directly with builders and product owners throughout the product and engineering lifecycle. Success is measured by the confidence and useful evidence the work gives the team. 

RESPONSIBILITIES 

• Use AI coding agents and related tools to explore the SDK codebases, understand behaviour and accelerate quality work.  
• Use product, technical, integration and release risk to identify the highest-value quality improvements.  
• Design, build and maintain useful unit and component tests for SDK behaviour.  
• Design, build and maintain integration tests covering SDK interaction with Storyteller services and APIs.  
• Put automated tests into active CI and release workflows, with failures that are clear and actionable.  
• Prove that tests detect meaningful breakage rather than merely producing passing output.  
• Investigate bugs using code, logs, APIs, test results and product behaviour.  
• Where appropriate, follow a well-understood issue through to a bounded production fix.  
• Work with builders to improve testability and prevent repeated classes of defects.  
• Turn repeated investigation or testing work into reusable AI workflows, tools, checks or test assets.  
• Communicate clearly in Slack and other team systems: state the evidence, impact, current understanding, owner and next action.  
• Contribute useful AI-assisted engineering practices to the wider SDK team while remaining primarily accountable for quality outcomes.  
• Contribute to tvOS, Android TV, Fire TV, Web and Roku as the role's scope expands.  

What Good Looks Like 

You will be succeeding when: 

• Important SDK behaviours have dependable automated protection;  
• Useful quality improvements land incrementally and are easy for the team to adopt;  
• SDK-to-platform integration failures are detected early and are easy to diagnose; 
• Tests run reliably in the workflows the team actually uses;  
• Test failures give the team enough information to act;  
• Well-understood defects are followed through to an appropriate fix or clear handover;  
• Repeated quality problems become better tests, tooling or product changes;  
• Automation and risk-based exploration provide evidence proportionate to the change;  
• Your communication makes quality risks and next actions easy to understand;  
• And the Storyteller team becomes more capable with AI because of practical workflows you have demonstrated through the work.  

QUALIFICATIONS 

What we’re looking for 

• AI-native execution – You use AI to do substantive technical work, not only to write or summarise. You can give an agent useful context, break down an ambiguous problem, inspect its work, redirect it when necessary and keep going when the first approach fails. 
• Quality and validation judgment – You can decide what is worth testing, recognise superficial tests and demonstrate that an automated test provides real protection. You think about false confidence, false positives, brittle tests, maintainability and failure diagnosis. 
• Agency and ownership – You can take an ambiguous quality risk, investigate it, gather evidence, choose a sensible next step and move the work towards a useful outcome. 
• Technical comfort – You are comfortable working with repositories, code, APIs, CI, logs, command-line tools and automated test frameworks. You can understand and challenge AI-generated code well enough to validate and land it safely. 
• Problem solving and systems thinking – You distinguish symptoms from root causes. When a problem repeats, you look for a reusable test, tool, check or workflow instead of treating every occurrence as a new manual task. 
• Clear communication – You communicate concisely and directly. You make evidence, impact, uncertainty, ownership and next actions visible without requiring someone else to reconstruct the situation. 
• Learning velocity – You are curious and can become effective in unfamiliar products, codebases and tools quickly. You change your approach when the evidence shows that it is not working. 

You do not need to be able to write Swift or Kotlin expertly without AI assistance, have deep experience across every Storyteller SDK platform, have held a traditional QA, test-automation or SDET title, or have managed a separate QA team. 
 

Nice to have 

• Experience with mobile applications or SDKs. 
• Experience with Swift or Kotlin. 
• Experience with API integration. 
• Experience with automated testing or test frameworks. 

These may help you become effective faster, but demonstrated AI-native execution, quality judgment and validation discipline matter more than matching a conventional technology checklist.

Is this You?

This role will likely suit you if: 

• You can take an ambiguous quality risk from signal to outcome with limited supervision. 
• You can frame the problem, direct AI and other tools effectively, and choose a practical testing or investigation approach. 
• You validate output rigorously, communicate what you found and land useful work. 
• You improve the system for the next occurrence rather than treating every problem as a one-off. 
• You are comfortable being accountable for quality outcomes while quality remains a shared responsibility across the team.

It probably isn’t right for you if: 

• You want AI to produce technical work that you do not need to understand, explain or validate. 
• You prefer to wait for fully defined instructions rather than investigate ambiguous risks and choose a sensible next step. 
• You are looking for a separate QA gatekeeping role rather than working directly with builders and product owners throughout the lifecycle. 

RECRUITMENT PROCESS

We keep the process straightforward and respectful of your time.  

1. Hiring Manager Conversation (20–30 mins)  

A short call to get to know you, share more about Storyteller and the QA Engineer role, and answer your questions.

There is no technical exercise at this stage. We’ll both aim to decide whether it makes sense to go deeper

2. Paid Take-home Task (~60 mins)  

A small, focused quality-engineering task that you can complete in your own time. We compensate you for completing it, regardless of the outcome.

You’re welcome to use AI tools. We’re interested in how you investigate unfamiliar software, identify meaningful risks, validate your work and explain your decisions—not how much code you can write from memory.

3. Review and CTO Interview (60–75 mins)  

We’ll review your submission together and discuss how you approached the task, what you tested, how you verified the results and any risks or limitations you identified.

You’ll also meet Dave, our CTO, and talk about how you investigate quality issues, use tools such as AI, communicate your findings and work with engineers to improve software quality.

And that’s it.

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