Uber
From Uber's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
What you'll do Own and drive GenAI-based quality engineering programs end to end, from problem definition and prototype through delivery and adoption.
We are the QA Tech Strategy & Automation team within Global Support Services (GSS) Quality Assurance. We make the quality of customer-support tools and experiences measurable, repeatable and AI-assisted.
We own test automation across mobile (Android/iOS), web and backend systems for multiple lines of business, and we run it on Uber's in-house automation platform as part of CI/CD and release validation.
We build and apply in-house GenAI solutions to quality engineering: AI-assisted test generation, self-healing tests, automated failure triage and risk-based test prioritization.
We partner closely with Engineering, Product, Operations and regional QA teams to catch defects at the source and raise release confidence, and we measure it: test coverage, maintenance effort, defect leakage and release reliability.
We are a collaborative, global team that favors experimentation: prototype quickly, measure accuracy and reliability, then scale what works.
Own and drive GenAI-based quality engineering programs end to end, from problem definition and prototype through delivery and adoption.
Build and deploy GenAI solutions (LLMs, agents and automation frameworks) for test generation, regression detection, failure analysis and coverage improvement.
Integrate these solutions into engineering workflows and CI/CD pipelines so quality signals reach teams early and automatically.
Turn complex quality problems into working prototypes, evaluate their accuracy and reliability, and work with engineering teams to scale the ones that succeed.
Define and track metrics for defect detection, testing efficiency, test maintenance effort and release quality, and report progress to stakeholders and leadership.
Work with Engineering, Product and QA leads to prioritize automation coverage, reduce defect leakage and strengthen release gating.
Contribute to the team's strategy, roadmap and strategy-of-record, and spread best practices in AI-assisted quality across teams.
5+ years in quality engineering, test automation or technical program management, with hands-on ownership of quality initiatives for large-scale software systems.
Demonstrated experience building and deploying GenAI solutions to quality engineering problems, such as test generation, regression detection, failure analysis and test coverage improvement.
Proficiency in Python or a similar language, with experience integrating LLMs, agents and automation frameworks into engineering workflows and CI/CD pipelines.
Ability to turn complex quality challenges into working prototypes, evaluate accuracy and reliability, and partner with engineering teams to scale them.
Proven ability to drive cross-functional programs from problem definition through delivery, using metrics to show improvements in defect detection, testing efficiency and release quality.
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