Company Description

Right-Hand Cybersecurity is building the next generation of Human Risk Management. We help enterprises understand and reduce workforce-related cybersecurity risk through intelligent security awareness, phishing simulations, AI-powered agents, behavioral insights, and risk analytics. We are a lean, product-driven team building for complex enterprise environments. Engineers work closely with product, design, customers, and company leadership—and have meaningful influence over what we build and how we build it.

The daily fulfillment of this mission would not be possible without the cornerstone of Right-Hand’s business - its people. We firmly believe that if we take care of our employees, they will take care of our customers, and the rest will work itself out. The founding team at Right-Hand has created a culture that evangelises six core values:

Resilience: We solve problems and overcome challenges to the best of our ability, regardless of how daunting they may appear.

Empathy: We listen to and understand the perspective of others, including our peers, customers, partners and investors.

Passion: We care deeply about our work, our customers, each other and this company.

Accountability: We trust each other to get our job done at a high level.

Transparency: We act and communicate openly, honestly, and effectively to drive toward positive outcomes.

Authenticity: We understand who we are and will surround ourselves with a diverse group of people who complement our skillset and share our values.

If you want to join this mission-driven team and believe you fit the profile below, let’s talk!

About the role

We are looking for a Quality Automation Engineer—not a traditional QA gatekeeper. You will build the systems, automation, and feedback loops that help the entire engineering team ship reliable software quickly. Developers will continue to own the quality of what they build; your responsibility is to make effective testing easy, fast, visible, and integrated throughout the development lifecycle. You will work alongside product engineers from early discovery and architecture through production release. You should be comfortable reading application code, challenging requirements, designing automation frameworks, investigating failures across services, and preventing defects rather than only detecting them before release. This is a hands-on engineering role with meaningful influence over our release process and quality architecture.

What you will do

Own our quality automation strategy

• Define a risk-based automation strategy across frontend, backend, integrations, data workflows, and AI-powered features.
• Determine which behaviors should be covered through unit, component, API, contract, integration, end-to-end, performance, or production-level testing.
• Ensure critical customer workflows are protected without creating a slow or excessively brittle test suite.
• Establish clear quality standards and release-readiness criteria.

Build scalable test automation

• Design and maintain automation frameworks for our React applications, Rails platform, APIs, and supporting services.
• Build reliable end-to-end automation using Playwright or an equivalent modern framework.
• Develop API, database, integration, and service-level tests that identify failures earlier than UI tests.
• Introduce contract testing for important internal and third-party service boundaries.
• Automate regression coverage for enterprise workflows, permissions, multi-tenancy, configuration, reporting, and integrations.
• Build reusable test-data factories, fixtures, mocks, and isolated test environments.
• Add performance, load, concurrency, and resilience testing for high-risk workflows.

Integrate quality into CI/CD

• Integrate automated tests into pull-request, staging, and release pipelines.
• Define practical quality gates based on risk, coverage, test results, and affected product areas.
• Optimize suites through parallelization, sharding, test selection, and appropriate separation of fast and extended tests.
• Keep pull-request feedback fast enough that engineers trust and use it.
• Identify, quarantine, investigate, and eliminate flaky tests.
• Ensure failures provide actionable diagnostic information rather than generic pipeline errors.
• Automatically connect production defects to permanent regression coverage.

Test security-sensitive workflows

• Build automated regression coverage for authentication, authorization, tenant isolation, role permissions, and sensitive-data access.
• Test negative and abuse scenarios, including cross-tenant access attempts and unauthorized API behavior.
• Partner with engineering and security teams to incorporate security regression tests into the delivery pipeline.
• Validate auditability, data integrity, and critical compliance-related behaviors.
• Help ensure quality automation supports—not bypasses—our security and SDLC controls.

Establish AI and agent quality practices

• Design evaluation frameworks for non-deterministic AI and agentic product behavior.
• Test prompt and model changes for regressions rather than relying only on exact-output assertions.
• Define representative evaluation datasets covering normal, edge, adversarial, and failure scenarios.
• Measure output quality, task completion, consistency, latency, cost, safety, and failure rates.
• Test tool use, permissions, fallback behavior, structured outputs, and human-in-the-loop controls.
• Build automated regression coverage for phishing content, training generation, conversational agents, and other AI-powered workflows.
• Distinguish genuine model-quality regressions from normal variation in generative output.

Use AI to improve quality engineering

• Use AI coding and testing tools to accelerate test-case design, automation development, codebase exploration, test-data creation, and failure investigation.
• Build workflows that turn requirements, production defects, and customer-reported issues into proposed test scenarios.
• Explore AI-assisted failure classification, duplicate detection, root-cause suggestions, and test maintenance.
• Critically review AI-generated tests for weak assertions, missing edge cases, security gaps, and false confidence.
• Document effective AI-assisted testing patterns and help the wider engineering team adopt them responsibly.

Create quality visibility

• Build dashboards that show automation coverage, pipeline health, flaky-test rate, escaped defects, failure trends, and release confidence.
• Identify recurring defect patterns and recommend product, architecture, or process improvements.
• Use production monitoring, logs, customer reports, and incident data to improve test priorities.
• Communicate quality risks clearly without becoming a release bottleneck.

What we are looking for

• Approximately 5+ years of experience in quality engineering, test automation, SDET, or software engineering roles.
• Demonstrated experience designing or significantly improving an automation framework—not only writing test cases within an existing one.
• Strong programming ability in JavaScript/TypeScript, Ruby, Python, or a comparable language.
• Hands-on experience with modern browser automation tools such as Playwright, Cypress, or Selenium.
• Strong experience testing APIs, databases, asynchronous workflows, and distributed services.
• Experience integrating automated tests and quality gates into CI/CD pipelines.
• Strong understanding of test architecture, test isolation, test data, mocking, coverage strategy, and failure diagnostics.
• Ability to read application code, understand system behavior, and investigate failures across multiple layers.
• Experience reducing flaky tests and improving automation execution time.
• Strong understanding of security, performance, reliability, and negative-path testing.
• Product judgment: the ability to identify what creates real customer risk rather than treating every test case as equally important.
• Strong written and verbal communication in a distributed environment.
• Comfort working with ambiguity and building quality practices in a fast-moving product organization.
• Practical experience using AI-assisted development or testing tools and independently validating their output.

Our technology

Our environment currently includes:

• React and JavaScript/TypeScript
• Ruby on Rails
• Python and Node.js services
• PostgreSQL, MongoDB, DynamoDB, Redis, and Sidekiq
• AWS, ECS/Fargate, S3, SQS, Lambda, and CloudFront
• CI/CD, automated security scanning, CloudWatch, and New Relic
• Enterprise integrations with email, identity, and cybersecurity platforms
• Large language models, generative AI, voice AI, and agentic workflows

You do not need experience with every technology. We care more about strong engineering fundamentals and your ability to design automation across unfamiliar systems.

What “AI-native quality engineering” means to us

AI-native quality engineering is not generating hundreds of shallow test scripts.

It means using AI to expand coverage, accelerate investigation, generate realistic data, uncover missing scenarios, and reduce repetitive work—while applying human judgment to decide what matters, whether assertions are meaningful, and whether the resulting automation provides genuine confidence.

The candidate must know when AI can accelerate the work and when deterministic engineering, security review, or exploratory testing is required. You may be an especially strong fit if

• You have been the first or senior Quality Engineer in a startup.
• You have built a quality automation function or framework from the ground up.
• You have introduced Playwright, API automation, contract testing, or containerized integration testing at scale.
• You have tested multi-tenant enterprise SaaS or cybersecurity products.
• You have experience testing AI agents or other non-deterministic systems.
• You have implemented performance testing using tools such as k6.
• You have built automated quality dashboards or production-feedback systems.
• You regularly identify specification, architecture, and testability issues before development begins.

This role may not be the right fit if

• Your primary focus is manually executing predefined regression scripts.
• You expect testing to begin only after engineering marks a feature complete.
• You consider quality the QA team’s responsibility rather than a shared engineering responsibility.
• You measure effectiveness by the number of test cases created instead of risk reduced.
• You depend entirely on UI-level automation.
• You accept AI-generated tests without reviewing their assertions, maintainability, and coverage.

What success looks like

Within your first three months, you will:

• Understand our architecture, critical customer journeys, release process, and existing automation.
• Produce a prioritized quality strategy based on customer and technical risk.
• Strengthen automated coverage for at least one critical product workflow.
• Improve test execution, diagnostics, or flaky-test visibility.
• Integrate with product development early enough to identify quality risks before release.

Within six months, you will:

• Establish a maintainable automation architecture across UI, API, integration, and service layers.
• Introduce measurable CI/CD quality gates without slowing engineering velocity.
• Establish baseline quality metrics and a visible quality-health dashboard.
• Create an evaluation approach for at least one AI-powered product area.
• Demonstrably reduce escaped regressions or manual release-validation effort.

How we evaluate candidates

1. A conversation about the quality systems and automation frameworks you have built.
2. A technical deep dive into an automation or reliability problem you personally owned.
3. A practical exercise involving a realistic product workflow, including test strategy and selected automation.
4. A discussion covering CI/CD, architecture, flaky tests, security, and AI-system evaluation.
5. A final conversation focused on ownership, product judgment, collaboration, and mutual fit.

Candidates may use the AI tools they normally use. We will evaluate how effectively they direct those tools, review the results, identify gaps, and remain accountable for the final solution.

Our Benefits

Yes, our benefits are great but we hope you want to work at Right-Hand because you believe in our mission to make humans more defensible against cyber threats.

• Competitive Salary
• Paid annual and medical leaves
• Medical benefit
• Flexibility in how and where you work
• Generous parental leave programs
• Wellbeing days
• and more...

Diversity and Inclusion are more than just words for us. Our commitment to inclusion across gender, race, ethnicity, sexual orientation, educational background, religion, ability, socioeconomic background, immigration statuses, and experiences drives us forward every day. We foster an open, tolerant, and constructive work environment with a team consisting of diverse views and backgrounds.

Right-Hand is an equal opportunity employer. All aspects of employment including the decision to hire and promote will be based on merit, competence, performance, and business needs. We encourage all interested applicants to apply.