About the role
About Wellspring
Wellspring Worldwide, Inc. is a leading provider of web-based software systems for managing research, technology commercialization, and innovation operations for universities, companies, government agencies, and independent labs. Founded in 2003, Wellspring has grown to serve over 500 organizations globally, including Fortune 500 companies, SMEs, hospitals, and universities. The company’s flagship products—Evolve, Astria, Sophia & Flintbox—enable organizations to manage the entire innovation lifecycle, from discovery and IP management to commercialization and knowledge transfer.
POSITION SUMMARY
We are hiring a Quality Engineer to help shape how quality is engineered in an AI-driven development environment. Quality Engineers at Wellspring operate with a high degree of autonomy and technical ownership, with meaningful cross-team influence. This role is ideal for an engineer who wants to remain hands-on, technically deep, and influential in shaping modern, AI-driven quality practices.
What You'll Do
Automated Quality Engineering
Design, implement, and maintain automated test suites using Playwright
Develop and maintain performance and load tests using JMeter
Create reusable test utilities, data strategies, and scalable testing patterns
Build and curate the shared foundation that AI-assisted authoring depends on so generated tests come out consistent
Keep the suite healthy as authoring volume increases
AI-Driven Quality Practices
Use AI-assisted tools to generate, refine, and maintain test cases, and test data
Direct coding agents through the full authoring loop providing context and reviewing output
Review generated tests with a critical eye for the ways they fail quietly
Use AI to find coverage gaps and propose edge cases, then apply your own judgment about which ones carry real risk
Use AI-assisted triage as a first pass on failures, separating likely regressions from flakes
Apply self-healing and auto-repair tooling deliberately, with guardrails, recognizing that a test which silently repairs itself can mask a genuine product regression
Apply risk-based testing to focus effort on business-critical and defect-prone areas
Validate AI-enabled product behavior for correctness, reliability, and unintended outcomes
Feed recurring defect and failure patterns back into shared context and conventions so the same class of miss does not recur
CI/CD & Engineering Integration
Integrate automated tests into GitHub Actions–based CI/CD pipelines
Partner with Engineering and DevOps to improve pipeline quality gates
Manage test scope and execution sequencing to balance coverage, risk, and feedback time
Write tests and failure output that are legible to agents as well as engineers
Ensure quality gates hold as change volume increases, so higher delivery throughput does not quietly become higher-confidence mistakes
Quality Leadership & Collaboration
Collaborate with Product, Engineering, and Architecture to clarify requirements and identify quality risks early
Propose and drive solutions to technical and testing challenges with cross-functional partners
Help establish team standards for AI-driven test authoring
Document and communicate testing strategies, tradeoffs, and outcomes
What We're Looking For
Required Experience & Skills
Experience as a Quality Engineer, SDET, or Test Automation Engineer on production systems
Strong hands-on experience with Playwright (UI and API testing)
Experience with performance and load testing using JMeter or similar tools
Solid programming skills in TypeScript/JavaScript, C#, Java, or Python
Experience integrating automated tests into CI/CD pipelines, preferably GitHub Actions
Working knowledge of modern architectures, including web applications, APIs, and cloud-based systems
Demonstrated, sustained daily use of at least one modern AI-assisted development tool
Ability to critically review AI-generated tests and code — identifying shallow coverage, incorrect assumptions, and plausible-looking output that does not hold up
Ways You'll Stand Out
Experience testing AI-enabled or data-driven systems
Experience designing automation frameworks or shared testing infrastructure
Familiarity with observability, monitoring, or quality metrics beyond pass/fail testing
Experience building evaluation harnesses or test strategies for non-deterministic systems
Experience structuring context for AI tooling — prompt design, retrieval, or packaging codebase and domain knowledge for code and test generation
A point of view on where AI-assisted testing genuinely helps
Originally posted on Himalayas