Senior Software Test Engineer III

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Requirements

PhD and 1 year of Software Engineering experience -OR- MS/MA and 3 years of Software Engineering experience -OR- BS/BA and 5 years of Software Engineering experience -OR AA and 14 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development -OR- HS/GED and 16 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development
U.S. Citizenship
Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing. In addition, applicants must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).
Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated.
This position is a Testing Designated Position (TDP). The candidate selected for this position will be subject to pre-employment and random drug testing for illegal drugs, including marijuana, consistent with the Controlled Substances Act and the PNNL Workplace Substance Abuse Program.
Mandatory Requirements: Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.

Nice To Haves

Degree in Computer Science, Software Engineering, or a related field
Experience implementing automated tests using Cypress.io, Playwright, or similar testing frameworks
Experience using AI-assisted development tools within an IDE, such as VS Code, to write automated tests and troubleshoot issues
Experience in JavaScript, Python programming languages
Knowledgeable in using SQL or GraphQL
Experience developing software test plans, test cases, and test reports
Knowledge of software engineering best practices and software development lifecycles
Experience with DevOps and MLOps, including automated tests within CI/CD processes such GitLab or GitHub
1+ years of experience using AI tools (i.e. Cline, Roo Code, etc.) within an IDE to write automated tests and/or troubleshoot issues
Familiarity with AI models such as Claude, Co-Pilot. Knowledgeable in using MCP servers, AI skills, and AI agents
Experience in validating models, data, and end-to-end workflows/integrations (APIs, databases, pipelines) using data/model validation plus integration, E2E, and regression testing, including handling non-deterministic outputs and real-world/edge/failure scenarios
Experience in assessing AI quality attributes (accuracy, precision/recall, relevance, bias/fairness, robustness/consistency), data quality (completeness, correctness, representativeness, drift, label quality), plus safety/explainability/guardrails and performance (latency, scalability, reliability)
Experience with cloud computing (AWS/Azure)
Familiar with containerization using Docker/Podman/Kubernetes
Strong analytical and troubleshooting skills with attention to detail
Excellent written and verbal communication skills

Responsibilities

Own and lead end-to-end test strategy for programs and/or multiple components, including scope, coverage goals, environments, release criteria, and quality gates
Partner with engineering, product, and stakeholders to refine requirements into testable acceptance criteria, traceability, and measurable quality outcomes
Drive risk-based test planning across functional, integration, system, regression, and performance testing; prioritize quality work based on mission impact
Produce executive-ready quality summaries (coverage, results, trends, residual risk) and recommend go/no-go decisions with clear rationale
Establish and improve test standards (definitions of done, test naming/structure, flake management, reporting expectations) across the team
Lead and coordinate testing efforts for a small team or a complex initiative; align work across developers, DevOps/platform teams, and data science partners
Mentor Level 1–2 engineers in test design, automation patterns, debugging, and quality communication; provide guidance through reviews and pairing
Champion a culture of quality: shift-left practices, defect prevention, and pragmatic automation that increases delivery speed and confidence
Influence roadmaps by identifying quality risks early and proposing mitigation plans (technical and process)
Architect and maintain automation across API, UI, integration, end-to-end, and regression layers; emphasize reliability and maintainability
Implement and standardize automated tests using Cypress.io, Playwright, or similar frameworks; drive down flakiness with robust test design and environment controls
Integrate test tooling into CI/CD pipelines (e.g., GitLab/GitHub) with reporting, metrics, and enforced quality gates
Validate workflows across APIs, databases, pipelines, and services using SQL and/or GraphQL where appropriate; ensure automation provides actionable diagnostics
Lead validation approaches for models, data, and end-to-end AI workflows, including strategies for non-deterministic outputs and regression detection
Define and operationalize AI quality attributes (accuracy, precision/recall, relevance, bias/fairness, robustness/consistency) and verify guardrails/safety/explainability expectations
Drive data quality validation (completeness, correctness, drift, representativeness, label quality) and incorporate checks into pipelines and release processes
Partner with engineers/data scientists to design repeatable evaluation harnesses and automated regression approaches aligned to mission needs
Work with AI agents/skills and MCP servers to support test automation workflows and system validation at scale
Lead quality practices for cloud and containerized deployments; apply strong working knowledge of cloud concepts (AWS/Azure) and container tooling (Docker/Podman, Kubernetes fundamentals)
Use observability (logs/metrics/traces) to debug failures, validate monitoring coverage, and improve testability and operational readiness
Drive performance and reliability validation (latency, scalability, stability) and ensure results feed back into design decisions and release gates
Serve as a quality leader with end users and stakeholders to prototype, configure, refine, verify, and troubleshoot systems to meet intended use
Evaluate and introduce new test tools/technologies; build adoption plans and standards that improve quality outcomes and team efficiency
Establish quality metrics and trend reporting (defect escape rate, test effectiveness, flake rate, automation ROI) and use them to guide improvements
Lead technical discussions and reviews around test strategy, design, and implementation; influence architecture for testability
Communicate complex technical risk clearly—written and verbal—tailored to engineers, leadership, and mission stakeholders
Incorporate feedback from defects and incidents to drive preventive actions and measurable improvements across teams and releases