AI QA & Research Lead
Why Work at Lenovo
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Description and Requirements
Key Responsibilities
Leadership & Coordination
- Lead and coordinate AI QA and research efforts across testers, software development engineer in test (SDET), and product stakeholders
- Act as the a point of alignment between QA teams and business needs
- Define priorities, assign tasks, and ensure timely delivery of high-impact outcomes
- Foster a culture of ownership, quality, and continuous improvement
AI QA Strategy & Execution
- Design and evolve AI-driven validation frameworks
- Ensure high standards for test quality, reliability, and actionable insights
- Adapt QA processes to operate efficiently under resource constraints
- Drive the adoption of state-of-the-art AI methodologies to the test area
Research & Innovation
- Guide research initiatives focused on AI QA and Automation tools
- Evaluate emerging technologies and assess their applicability to real-world problems
- Translate research findings into deployable solutions
- Collaborate with academic partners to leverage cutting-edge innovation
Automation & Tooling
- Oversee the development and evolution of internal QA tools (e.g., Automation Hub)
- Ensure tools are scalable, maintainable, and aligned with user needs
- Define quality criteria and evaluate technical solutions
Cross-functional Collaboration
- Partner with Product, Engineering, and QA teams to understand requirements and constraints
- Support decision-making with data-driven insights and validated results
What We Are Looking For (Core Qualifications)
Must-have Skills & Experience
- Strong experience in mobile software testing and QA processes
- Advanced Python coding
- Hands-on experience with test automation frameworks and tools
- Solid understanding of AI/ML concepts and their application in QA
- Proven ability to lead technical initiatives and coordinate cross-functional teams
- Experience translating research or experimental work into production-ready solutions
- Strong analytical thinking and problem-solving skills
Nice-to-have
- Experience with AI model evaluation, validation pipelines, or LLM testing
- Background in applied research or collaboration with academic institutions
- Familiarity with data analysis and experimentation methodologies
- Experience working in resource-constrained or high-ambiguity environments
Key Competencies
- Systems thinking: ability to connect strategy, execution, and outcomes
- Technical leadership: influence without authority across teams
- Adaptability: thrive in evolving environments with changing constraints
- Communication: articulate complex ideas clearly to diverse audiences
- Innovation mindset: continuously explore and apply new approaches
Success Measures (What Good Looks Like)
- High-quality, scalable AI QA processes are established and continuously improved
- Teams operate efficiently despite resource constraints
- Research initiatives result in tangible improvements to tools and workflows
- Strong alignment across QA, Engineering, and Product teams
- Measurable impact on validation accuracy, speed, and decision-making quality
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