Quality Assurance Automation Engineer

Turba Labs; Heidelberg; On-site; EUR 70,000 - 95,000; Full time

Job summary

Turba Labs in Heidelberg, Germany, is seeking a QA Automation Engineer to advance testing for our AI infrastructure platform, including scheduler and simulator components. You will craft automated tests for APIs, services, and system-level workflows, and help shape release readiness while improving CI/CD quality gates.

Ideal candidates have 5+ years of QA/test automation experience in infra or cloud contexts, with Java or Python/Pytest, and strong Kubernetes/Linux/CI/CD knowledge.

Qualifications

• 5+ years in QA, SDET, or test automation for infra/platform contexts.
• Hands-on automation in Java or Python/Pytest with clean, maintainable code.
• Experience building tests across APIs, services, UI, and end-to-end or system-level scenarios.
• Strong Kubernetes, containers, Linux, deployments, logs, metrics, and CI/CD understanding.
• Experience with manual exploratory testing and risk-based release decisions.
• Experience integrating tests into CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, CircleCI).
• Ability to diagnose failures and distinguish product vs environment issues.
• Excellent communication with backend/platform teams; senior ownership.

Responsibilities

• Design, build, and maintain automated tests across scheduler, simulator, APIs, backend services, frontend workflows, integrations, and system-level scenarios.
• Develop reliable regression, smoke, integration, end-to-end, and release test suites that run in CI/CD pipelines and validate Kubernetes-based deployments.
• Test AI infrastructure workflows for GPU datacenters, heterogeneous resource allocation, configuration changes, failure scenarios, monitoring signals, and rollback behavior.
• Perform manual exploratory, functional, and regression testing where automation is not yet available or where senior judgment is required for complex infrastructure behavior.
• Collaborate with engineers, product managers, and platform teams to define test strategy, quality criteria, release readiness, reproducible simulation scenarios, and customer-environment risk areas.
• Analyze test results, investigate failures, distinguish product defects from environment, infrastructure, data, or test issues, and drive root-cause resolution.
• Improve CI/CD quality gates, test reporting, flaky-test handling, test data management, environment validation, and automated checks for Kubernetes deployments.
• Contribute to test framework architecture, tooling choices, Python coding standards, and maintainable automation practices.
• Document test plans, risks, coverage gaps, simulation assumptions, infrastructure constraints, and release recommendations clearly for technical and non-technical stakeholders.
• Mentor engineers and QA colleagues on automation best practices, testability, infrastructure-aware QA, and quality ownership.

Skills

Test automation

Java

Python/Pytest

CI/CD

Communication

Senior ownership

Tools

Docker

Kubernetes

GitHub Actions

GitLab CI

Jenkins

CircleCI

Terraform

Observability tools

Job description

We are looking for a QA Automation Engineer to drive quality for our AI infrastructure platform. We build scheduler and simulator products that work directly with customer datacenter infrastructure for GPUs, helping customers operate and simulate usage across mostly heterogeneous environments.

This role requires strong test automation, practical manual testing judgment, Kubernetes and infrastructure understanding, and CI/CD experience. You will help define how we test scheduler and simulator behavior, what we automate, and how quality gates become part of everyday development and release workflows.

Responsibilities

• Design, build, and maintain automated tests across scheduler, simulator, APIs, backend services, frontend workflows, integrations, and system-level scenarios.
• Develop reliable regression, smoke, integration, end-to-end, and release test suites that run in CI/CD pipelines and validate Kubernetes-based deployments.
• Test AI infrastructure workflows for GPU datacenters, heterogeneous resource allocation, configuration changes, failure scenarios, monitoring signals, and rollback behavior.
• Perform manual exploratory, functional, and regression testing where automation is not yet available or where senior judgment is required for complex infrastructure behavior.
• Collaborate with engineers, product managers, and platform teams to define test strategy, quality criteria, release readiness, reproducible simulation scenarios, and customer-environment risk areas.
• Analyze test results, investigate failures, distinguish product defects from environment, infrastructure, data, or test issues, and drive root-cause resolution.
• Improve CI/CD quality gates, test reporting, flaky-test handling, test data management, environment validation, and automated checks for Kubernetes deployments.
• Contribute to test framework architecture, tooling choices, Python coding standards, and maintainable automation practices.
• Document test plans, risks, coverage gaps, simulation assumptions, infrastructure constraints, and release recommendations clearly for technical and non-technical stakeholders.
• Mentor engineers and QA colleagues on automation best practices, testability, infrastructure-aware QA, and quality ownership.

What we're looking for

• 5+ years of professional QA, SDET, or test automation experience, ideally in infrastructure, platform, cloud, developer tooling, distributed systems, or deep-tech product environments.
• Strong hands-on test automation experience, especially with Java OR Python/Pytest; ability to write clean, maintainable test code and tooling.
• Experience building automated tests across APIs, services, integrations, UI workflows, and end-to-end or system-level scenarios.
• Solid understanding of Kubernetes, containers, Linux environments, deployments, configuration, logs, metrics, monitoring, and CI/CD pipelines.
• Ability to test complex infrastructure products where behavior depends on resource allocation, workload characteristics, environment configuration, and distributed system interactions.
• Experience with manual exploratory testing, defect isolation, risk-based release decisions, and investigating ambiguous failures in technical systems.
• Practical experience integrating automated tests into CI/CD pipelines such as GitHub Actions, GitLab CI, Jenkins, CircleCI, or similar.
• Experience debugging failures using logs, metrics, traces, screenshots, test artifacts, Kubernetes events, container logs, and developer tools.
• Strong communication skills and the ability to work closely with backend, platform, infrastructure, and product teams to improve testability and prevent defects early.
• Senior-level ownership: able to define priorities, challenge assumptions, improve processes, and deliver without detailed supervision.

Nice to have

• Experience testing AI infrastructure, GPU platforms, schedulers, simulators, cloud platforms, infrastructure products, developer tools, data platforms, or complex distributed systems.
• Experience with Docker, Kubernetes, Terraform, Helm, observability tooling, Linux, networking basics, or platform engineering workflows.
• Experience testing heterogeneous environments with different GPU types, node configurations, workloads, tenants, quotas, or capacity constraints.
• Experience with performance, load, scalability, reliability, contract, security, or chaos testing.
• Experience building test frameworks, internal QA tooling, synthetic workload generators, simulators, or test data pipelines from scratch.
• Experience with test management, reporting, quality analytics, and release dashboards.
• Experience coaching teams toward better quality ownership, shift-left testing, and infrastructure-aware QA practices.