Staff Applied Scientist

At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high-performing teams, and design products people love. But we are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close-knit, high-functioning teams with an unwavering dedication to serving our customers. When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.   Staff Applied Scientist   Why We Have This Role We are looking for a talented and innovative Staff Applied Scientist to bring our Machine Learning and Artificial Intelligence R&D and strategy to the next level. Our goal is to personalize the Qualtrics experience using ML and AI features showcasing Qualtrics data as a core value proposition and competitive advantage. This team partners with application teams to deliver ML algorithms and models that drive efficiency, innovation, and growth. They identify business needs and define strategies where Qualtrics can most benefit from AI technologies and they conduct ML modeling with deep expertise and rigor, including model selection, training, fine-tuning, customization, explainability, and evaluation. How You’ll Find Success Leverage your deep knowledge of artificial intelligence (AI) principles, including machine learning, natural language processing, computer vision, and reinforcement learning. Use your understanding of both supervised and unsupervised learning techniques, and their applications in building intelligent systems. Develop and optimize algorithms for building scalable and efficient GenAI applications. Tackle challenging problems in creative ways, leveraging generative models to address real-world use cases and drive innovation. Use effective communication skills to articulate technical concepts to non-technical stakeholders and gather requirements for GenAI application development. Design and execute sophisticated evaluation strategies for agentic systems, including defining rubric-based success criteria, multi-turn conversation simulation, and implementing LLM-as-a-judge frameworks. Show strong programming skills in languages like Python, along with proficiency in deep learning frameworks such as TensorFlow, PyTorch, or similar. How You’ll Grow Passion for leveraging cutting-edge AI technology to create innovative GenAI applications that have a meaningful impact on businesses, industries, and society. Commitment to developing GenAI applications that adhere to ethical standards and promote positive societal impact while minimizing potential risks. Drive to push the boundaries of what's possible with AI, and to contribute to the advancement of the field through research, experimentation, and collaboration. Willingness to stay updated with the latest advancements in AI research and technology, and to continuously learn and adapt to new methodologies and best practices. Agility to pivot and iterate on GenAI applications based on feedback, emerging trends, and changing business requirements. Things You’ll Do Work as part of a multidisciplinary team to research, implement, evaluate, optimize, productize and maintain cutting-edge machine learning models to meet the demands of our rapidly growing business Stay on top of the latest developments in machine learning and related research, and present research findings with the broader community Work closely with, and incorporate feedback from other specialists, engineers, and product managers Lead and engage in design reviews, modeling discussions, requirement definitions and other technical activities in diverse capacity Mentor and grow junior scientists, drive best practices for experimentation, reproducibility, monitoring and lifecycle management, and ensure models are reliable, scalable and impactful in production Champion Evaluation-Driven Development (EDD) by embedding automated testing, risk-based assessments, and production monitoring into the full agentic lifecycle. What We’re Looking For On Your Resume Bachelors and Ph.D in Computer Science or related fields 7+ years of industrial research experience in machine learning, NLP, information retrieval, deep learning or a related field. Deep learning implementation expertise (MxNet, TensorFlow, PyTorch etc) Excellent communication, writing and presentation skills Excellent command of at least one modern programming language (preferably Python) Excellent problem solving ability Deep understanding of machine learning model life cycle management Depth in one or more of the following: Natural Language processing, information retrieval, speech processing, deep learning, reinforcement learning, etc. Knowledge of or experience in building production quality and large scale deployment of applications related to machine learning Comfortable working in a fast paced, highly collaborative, dynamic work environment. Experience in machine learning systems (e.g. SageMaker, MLFlow), and deep learning frameworks  (e.g. TensorFlow, PyTorch, MXNet etc) Preference for a publication record in top-tier ML and NLP conferences (e.g. NeurIPS, ICML, SIGIR, ICLR, ACL, EMNLP, etc.) Proven track record in evaluating complex, multi-turn agentic systems. Deep experience with observability tools, evaluating tool-use reliability, and implementing systematic benchmarking in CI/CD pipelines. What You Should Know Ab