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Qualtrics is seeking an Applied Scientist II to advance its Core AI and Machine Learning R&D strategy. The role focuses on building cutting-edge predictive models and generative AI applications that personalize the Qualtrics experience and serve as a core competitive advantage across the platform's 18,000+ global clients.
You will leverage deep expertise in AI principles—including machine learning, natural language processing, computer vision, and reinforcement learning—to develop and optimize algorithms for scalable GenAI applications. Key responsibilities include addressing product challenges through Large Language Models and deep learning approaches, designing and evaluating Agentic AI systems, and publishing research findings. You'll work within a multidisciplinary team alongside engineers, product managers, and specialists to research, implement, evaluate, and productize machine learning models that meet rapidly growing business demands.
The role requires strong technical execution: developing GenAI applications that adhere to ethical standards, staying current with cutting-edge AI research, and contributing to the Conversational AI, NLP, and Data Science technology roadmap. You will lead design reviews, modeling discussions, and requirement definitions while incorporating feedback from cross-functional partners. Success in this position demands the ability to communicate complex technical concepts to non-technical stakeholders, pivot based on feedback and emerging trends, and push the boundaries of what's possible with AI.
Required qualifications include a Bachelor's degree and Ph.D. in Computer Science or related field, 3+ years of combined academic and industrial research experience in machine learning, NLP, information retrieval, or deep learning, and hands-on experience with Agentic AI systems. You must demonstrate deep learning implementation expertise (TensorFlow, PyTorch, or similar), excellent command of Python or another modern programming language, and a solid understanding of machine learning fundamentals and the tool ecosystem. Experience with model lifecycle management, evaluation frameworks, and production ML systems is essential.
