Senior AI-Native SDET

We are currently looking for an experienced Senior AI-Native SDET.

Mercury Development is a software engineering company specializing in custom solutions for US-based businesses, from large enterprises to fast-growing tech startups. Our apps are used by more than 50 million people worldwide, and many of our products have been featured in the App Store and covered by TechCrunch, Engadget, and Gizmodo. We are a global team of 500+ talented professionals working on a wide range of challenging projects.

We are looking for a Senior AI-Native SDET to develop and evolve test automation using AI agents. In this role you will connect agents to code, test data and CI, assign engineering tasks to agents and validate their results. You will also be responsible for test architecture and the stability of test runs in CI.

You will choose technical approaches, make architectural decisions and deliver them into working solutions. Working with AI agents requires determining available context and tools, task decomposition, and validating outcomes. You will also handle complex technical problems.

• Define test coverage strategy and architecture for API, UI and E2E tests for web and mobile applications.
• Design and implement workflows for AI agents: task briefs, access to tools and data, agent run scenarios and automated result checks.
• Design and implement the most complex checks, shared components and integrations within the test harness.
• Make architectural decisions, plan migrations and manage technical debt without halting product delivery.
• Ensure reliability of CI runs: test data, parallelism, observability, reports and artifacts.
• Identify systemic causes of instability and resolve issues at the boundaries of tests, product and infrastructure.
• Maintain automation code: perform refactoring, reduce technical debt and control quality of changes.
• Validate technical solutions in practice through code, targeted runs, logs and metrics.

• Practical experience using AI agents for complex engineering tasks and building repeatable workflows: context management, constraints, decomposition, control, quality gates and result verification.
• Commercial responsibility for an automation subsystem or a large test harness operating in regular CI.
• Strong programming skills in TypeScript/JavaScript or Python: module and API design, readability, extensibility, testability and code review.
• Deep hands-on experience with at least one UI/E2E framework and with API automation.
• Ability to balance API, UI and E2E coverage considering product risks, development and maintenance costs.
• Experience designing test data, isolating runs and building reproducible environments.
• Confident use of Git, CI/CD, Docker, logs, metrics, reports and artifacts.
• Experience diagnosing systemic instability: flaky tests, races, performance issues, environment problems and external dependencies.
• Ability to document technical decisions in writing and explain chosen approaches.
• Technical English for working with requirements, documentation and code.

AI agents are embedded in our daily work: we use them to explore code and docs, develop and maintain tests, work with data and CI, analyze logs and update documentation. For us, an AI-native approach means defining the scope of a task, preparing context and tools for the agent, selecting control points and verifying results. The engineer retains responsibility for architecture, quality and final outcomes.

AI tools: Codex, Claude Code, Copilot/Cursor and MCP integrations. The rest of the stack depends on the project: TypeScript/JavaScript, Python. Playwright, Detox, Appium, Patrol, Pytest. GitLab CI/CD, Docker, Allure, REST API, SQL and AWS.

• Experience creating or significantly refactoring a test framework and safely migrating existing coverage.
• Experience building custom MCP integrations, tools or quality gates for AI agents.
• Automation experience with React Native, native iOS/Android or Flutter apps and understanding limitations of different mobile stacks.
• Experience with parallel and distributed runs, data isolation, test account factories and managing CI cost.
• Load testing, visual regression or other specialized automated checks.
• Designing test infrastructure using AWS services.