AI QA & Research Lead

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 #MBG