About the Role
Join an early-stage AI infrastructure company as its first QA engineer, owning quality across a Kubernetes-based GPU fleet management platform. You will test conversational and web interfaces alongside the backend systems that manage production clusters, helping ensure that user requests are handled accurately and safely.
What You’ll Do

• Test the conversational interface end to end, including command accuracy and the actions it performs on live clusters.

• Build evaluations for AI agent behavior, checking answer quality, safe actions, and handling of ambiguous or invalid requests.

• Develop end-to-end tests for the web interface and verify consistent behavior across interfaces.

• Write Go integration tests for multicluster backend workflows, including installation, upgrades, and failover.

• Create and tear down Kubernetes test clusters to validate controllers, operators, and custom resources across Kubernetes versions.

• Verify alert delivery across monitoring and user interfaces, and ensure alerts trigger appropriately.

• Gate releases in CI and turn bugs and incidents into regression tests.

What We’re Looking For

• At least 3 years of experience in QA, SDET, or test automation, building or delivering test suites and automation frameworks.

• Strong Go experience writing integration tests or backend test automation, and experience building end-to-end web UI tests.

• Hands-on experience with Kubernetes, React, RPC systems such as gRPC, CI/CD, and Linux fundamentals.

• Experience testing infrastructure or distributed systems; familiarity with multicluster tooling, chaos engineering, or Kubernetes testing tools is useful.

• Familiarity with Prometheus, Alertmanager, Grafana, Playwright, Slack apps, or GPU and bare-metal infrastructure is a plus.

• Experience building test infrastructure from scratch or evaluating LLM-powered and conversational products is valued.

Compensation & Benefits
Base salary: $180,000 to $200,000 annually. Visa sponsorship is not available.
Location
On-site in San Francisco, California, United States.

About the Role
Join an early-stage AI infrastructure company as its first QA engineer, owning quality across a Kubernetes-based GPU fleet management platform. You will test conversational and web interfaces alongside the backend systems that manage production clusters, helping ensure that user requests are handled accurately and safely.
What You’ll Do

• Test the conversational interface end to end, including command accuracy and the actions it performs on live clusters.

• Build evaluations for AI agent behavior, checking answer quality, safe actions, and handling of ambiguous or invalid requests.

• Develop end-to-end tests for the web interface and verify consistent behavior across interfaces.

• Write Go integration tests for multicluster backend workflows, including installation, upgrades, and failover.

• Create and tear down Kubernetes test clusters to validate controllers, operators, and custom resources across Kubernetes versions.

• Verify alert delivery across monitoring and user interfaces, and ensure alerts trigger appropriately.

• Gate releases in CI and turn bugs and incidents into regression tests.

What We’re Looking For

• At least 3 years of experience in QA, SDET, or test automation, building or delivering test suites and automation frameworks.

• Strong Go experience writing integration tests or backend test automation, and experience building end-to-end web UI tests.

• Hands-on experience with Kubernetes, React, RPC systems such as gRPC, CI/CD, and Linux fundamentals.

• Experience testing infrastructure or distributed systems; familiarity with multicluster tooling, chaos engineering, or Kubernetes testing tools is useful.

• Familiarity with Prometheus, Alertmanager, Grafana, Playwright, Slack apps, or GPU and bare-metal infrastructure is a plus.

• Experience building test infrastructure from scratch or evaluating LLM-powered and conversational products is valued.

Compensation & Benefits
Base salary: $180,000 to $200,000 annually. Visa sponsorship is not available.
Location
On-site in San Francisco, California, United States.