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BRAYN is building a new category of growth platforms: mobile-first, gamified, AI-powered.
Inside BRAYN, learning feels like gameplay, progress becomes measurable proof, and AI mentors guide users through real decisions and career development.
We ship fast, iterate constantly, and experiment across multiple product layers — from mobile UX and AI coaching to experimentation frameworks and new product mechanics.
We’re looking for a product-minded QA Engineer who wants to help the team ship faster with confidence.
This role is not about running checklists.
It’s about owning quality across mobile, API, AI features, and release cycles.
What you’ll work on:
Mobile product quality (iOS-first)
Functional testing of the iOS app across features and releases
Regression testing for product changes and new feature launches
Testing across different devices, OS versions, and network conditions
Validation of UI logic, animations, reward systems, and interaction flows
BRAYN is a UX-sensitive product, so attention to experience quality matters.
API & system behaviour
You’ll validate product logic beyond the UI layer.
This includes:
API request/response validation
auth/session flows
caching behaviour
retries and idempotency
integration edge cases
failure scenarios
AI-powered features
BRAYN includes AI mentors and AI-driven product features.
Testing these systems requires thinking differently from traditional deterministic testing.
You will work with:
non-deterministic outputs
fallback logic
timeouts and degraded states
prompt variations
output boundaries and safety behaviour
You should think in behavior patterns, not exact expected strings.
Release confidence
You will help the team ship fast without losing control over quality.
This includes:
pre-release validation
TestFlight checks
crash signal review
release risk assessment
production sanity checks
QA helps the team move faster safely.
Product signals & analytics
Quality in modern products includes validating signals.
You may validate:
analytics events
product funnels
feature triggers
unlock mechanics
experiment conditions
Feature flags & experimentation
BRAYN actively experiments.
You’ll test:
feature flags
staged rollouts
experiment logic
fallback behaviour
Admin panel & CMS
BRAYN includes operational tools used by the team.
You will test:
content flows
configuration logic
admin features
Quality systems
Beyond testing features, you’ll help improve how the team works with quality.
Examples:
test checklists
reusable QA scenarios
release validation routines
QA documentation
What the role looks like in practice
Approximately:
80–90%
manual product testing
API testing
release validation
edge-case discovery
10–20%
automation-related work
AI-assisted QA workflows
test scenario design
collaboration with the whole product team
You don’t need to be an automation engineer, but you should think about scalable quality patterns.
Tools you’ll likely work with:
iOS builds / TestFlight
Postman
Charles / network debugging tools
Sentry or crash monitoring tools
Notion / task documentation
AI tools (ChatGPT, Claude, etc.)
AI awareness
BRAYN is an AI-native product.
We expect QA engineers to work comfortably with modern AI workflows.
You should be able to:
understand how LLM-based systems behave
test behaviour under uncertainty
explore edge cases with AI assistance
generate scenarios and analyze failures with AI tools
AI should make your QA work faster and deeper.
Nice to have:
experience testing mobile subscriptions or purchase flows
familiarity with feature flags / experimentation systems
exposure to analytics validation
basic performance awareness (latency, degraded UX)
some exposure to test automation or scripting
Languages
Strong written and spoken communication in:
English — used for documentation and product collaboration
Russian — used in internal team communication
What we value:
Product intuition
Curiosity and ownership
Clean communication
Comfort with ambiguity
Thinking about quality as a system, not a checklist
What success looks like in this role:
Within the first months you should help the team:
detect risks earlier in development
improve release confidence
reduce production issues
increase speed without sacrificing quality