QA Automation Lead

About this role

We live in a world where technology is rapidly changing the educational experiences of students and teachers everywhere, and we have the opportunity to shape how this change takes place. Kira's mission is to harness transformative AI technologies to make world-class personalized teaching and learning accessible to everyone. Kira is a rapidly growing startup backed by top-tier Venture Capital funds including New Enterprise Associates (NEA), Andrew Ng’s https://aifund.ai/AI Fund, and Primavera.

As our QA Automation Lead, you'll own and elevate the quality bar across our platform, and build the team that sustains it. This is a player-coach role: you'll spend most of your time hands-on (designing automation, performance testing, and quality systems) while also mentoring and, over time, growing and leading a small QA team.

This is not a traditional QA role. We're looking for a senior, pragmatic engineer who is first and foremost a doer with management experience -- someone who ensures high product quality for teachers and students while modernizing how we approach testing, performance, and release confidence, including thoughtfully applying AI tools to accelerate test creation, maintenance, and coverage. You'll be the dedicated owner of end-to-end test coverage, performance test scaffolding, and load testing, and you'll have at least one QA engineer reporting to you.

This is a full-time position that requires flexibility with work hours and availability outside of regular business hours. We have offices in San Francisco, CA and New York City, NY and we ask that employees work from one of the two locations and come into the office in-person 4 days a week (Wednesdays WFH).

WHAT YOU’LL DO

- Own and evolve our quality strategy across frontend, backend, and AI services, and be the dedicated owner of end-to-end test coverage, performance test scaffolding, and load testing

- Lead and grow a small QA team — mentoring engineers, setting standards, and managing at least one direct report — while staying hands-on yourself

- Partner closely with product and engineering to define clear acceptance criteria and prevent defects early

- Design and implement scalable automated testing across end-to-end, integration, regression, and performance layers

- Own performance and load testing for critical workflows — login surges, classroom activity, assignment submissions, real-time features, and AI services — building realistic workload models from production usage, establishing baselines and capacity targets, and validating the platform ahead of peak usage

- Diagnose performance bottlenecks across test infrastructure, networks, application services, caches, databases, and third-party dependencies, and partner with engineering on remediation

- Evaluate and adopt modern AI-powered tools to accelerate test generation, maintenance, debugging, and coverage

- Improve release confidence through stronger CI/CD integration, test observability, and failure diagnostics, and establish pragmatic quality gates that improve reliability without slowing velocity

- Lead post-incident quality improvements, help prevent regressions, and contribute to documentation and runbooks

- Flex into hands-on manual QA when needed (~10–20% of the time)

WHAT WE’RE LOOKING FOR

- 8+ years in quality engineering, test automation, or SDET roles, with experience mentoring or leading engineers

- Automation architect mindset — you design and build quality systems, not just execute tests

- Product partnership — a track record of working closely with product to shape requirements, spot edge cases, and prevent defects early

- Complex UI/UX platform experience — you've owned quality for a sophisticated, user-facing web platform

- Performance and load testing experience — hands-on with tools such as k6, Artillery, Gatling, JMeter, or Locust, including designing workload models and interpreting latency percentiles, throughput, concurrency, and resource saturation

- Strong experience with modern testing frameworks such as Playwright, Cypress, Jest, JUnit, or PyTest, and building automated E2E suites in CI/CD

- Strong debugging skills across a multi-layer stack (ours: Next.js/React frontends, Kotlin/Java Spring Boot backends, Python FastAPI AI services, LangSmith agent workflows), with familiarity testing distributed systems, APIs, and database-backed applications

- Experience defining or improving QA processes in a fast-paced startup environment, with practical judgment about when to automate, when to test manually, and when to push back

- Cloud experience, ideally AWS; clear communication across technical and non-technical audiences; high attention to detail and strong product intuition

NICE TO HAVE

- Experience testing AI- or LLM-backed features — evaluating nondeterministic outputs, prompt regression testing, latency budgets, and graceful degradation when model providers have issues

- Experience with application performance monitoring, distributed tracing, and cloud infrastructure metrics

- Experience building internal tooling that improves developer velocity

WHAT SUCCESS LOOKS LIKE

- Fewer high-severity bugs reaching production and greater automated coverage of critical workflows

- Repeatable performance tests that identify scaling risks before they affect teachers and students, with clear baselines and capacity expectations for peak usage

- Faster, more confident releases and quality standards engineers respect rather than bypass

- A growing QA function and AI-assisted processes that meaningfully reduce manual effort

WHAT WE OFFER

- Competitive compensation and equity. For applicants in the US, the salary range for this role is $175,000 – $210,000, depending on relevant experience and skill set

- Medical, dental, vision and life insurance, including a medical insurance plan with 100% coverage of employee premiums

- Daily lunch credit through DoorDash in office + free Das

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