Software Test Engineer II

Overview

At PNNL, our core capabilities are divided among major departments referred to as Directorates within the Lab. Each Directorate focuses on a specific area of scientific research or other function, has its own leadership team, and a dedicated budget. The Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we host the Environmental Molecular Sciences Laboratory, a Department of Energy Office of Science user facility on the PNNL campus.

The National Security Directorate (NSD) drives science‑based, mission‑focused solutions to address complex real‑world threats to our nation and the world.

We are seeking a Software Test Engineer to join PNNL’s Test Ops team, ensuring the quality, reliability, and performance of innovative systems that span agentic AI platforms, large‑scale data orchestration, and real‑time intelligence processing. This role offers mid‑career test engineers the chance to apply modern QA and full‑stack test engineering practices to mission‑critical national security applications, strengthening expertise in end‑to‑end system validation across APIs, data pipelines, and production‑like environments.

You’re a motivated test engineer with experience developing test strategies and test artifacts—test plans, test cases, and test reports—and building or maintaining test automation. You have strong fundamentals in software engineering and QA best practices, partner across the full development lifecycle to translate requirements into measurable acceptance criteria and comprehensive coverage, and communicate clearly with both technical and non‑technical stakeholders.

• Design, develop, document, execute, and debug test strategies for new and existing software systems, applications, and hardware/software interfaces, applying QA best practices.
• Collaborate with cross‑functional teams throughout the development lifecycle to analyze user needs and requirements; translate requirements into test plans, test cases, traceability, and acceptance criteria.
• Ensure comprehensive functional, integration, system, regression, and performance coverage using risk‑based approaches and clear release criteria.
• Produce high‑quality test reports and quality summaries that communicate coverage, results, and risk.

• Build and maintain automated and manual test solutions across API, UI, integration, end‑to‑end, and regression layers.
• Implement automated tests using Cypress.io, Playwright, or similar frameworks; reduce flakiness and improve reliability.
• Integrate test tooling and automated tests into CI/CD pipelines (e.g., Git Lab or Git Hub), including reporting and quality gates.
• Validate end‑to‑end workflows and integrations across APIs, databases, pipelines, and services using SQL and/or Graph
  
  QL where appropriate.

• Validate models, data, and end‑to‑end workflows using data/model validation plus integration, E2E, and regression testing, including handling non‑deterministic outputs.
• Assess AI quality attributes such as accuracy, precision/recall, relevance, bias/fairness, robustness/consistency, and verify guardrails, safety, and explainability expectations.
• Evaluate data quality signals including completeness, correctness, representativeness, drift, and label quality.
• Partner with engineers to define and automate AI evaluation and regression approaches that fit mission needs.
• Work with AI agents/skills and MCP servers to support test automation workflows and system validation.

• Contribute to quality practices for cloud and containerized deployments by applying a general understanding of cloud concepts (e.g., AWS/Azure services) and common container tooling (Docker/Podman, Kubernetes fundamentals).
• Use observability (logs, metrics, traces) to debug failures, validate monitoring, and improve system testability.
• Support performance testing and reliability validation (latency, scalability, stability) for…