Top AI & Machine Learning Jobs

Design and prototype AI-native, agentic user experiences by delivering coded prototypes (React/TypeScript), leveraging LLMs and generative tools, and partnering with product and engineering to ship scalable, data-driven AI features and design system primitives across the platform.

Lead development of a Semantic Brain: map raw event logs to industry ontologies, build concept/identity-anchored graphs, design reward extraction and off-policy evaluation, deploy agentic LLM orchestration and scalable data pipelines to enable autonomous enterprise decisioning.

Lead strategy and execution to evolve a mature text analysis engine into a hybrid LLM/classical ML architecture. Manage and mentor a team of product managers, partner with applied science and engineering, define multi-year roadmaps, establish evaluation frameworks, and balance cost, latency, accuracy, and compliance while driving scalable text analytics across the Qualtrics platform.

Lead product strategy and roadmap for Qualtrics' Core AI Platform, building foundational capabilities (ontologies, agents, model infra, retrieval, observability, safety). Partner cross-functionally to define architecture, prioritize investments, drive implementation, and measure platform adoption, reliability, and AI quality for enterprise use.

Lead research and engineering of production-grade ML/AI models (NLP, generative and agentic systems). Design, evaluate, and deploy scalable models, implement evaluation-driven development, mentor junior scientists, collaborate with product and engineering, and embed observability and CI/CD benchmarking to ensure reliable, ethical, and high-impact AI features.

Research, develop, and productize scalable AI/ML and GenAI solutions (LLMs, NLP, agentic systems). Build, evaluate, optimize, and deploy deep learning models, collaborate with engineers and product teams, publish research, and contribute to Conversational AI and data science roadmaps.

Lead research and productization of ML/AI solutions across Qualtrics: design, train, fine-tune, evaluate, and deploy generative and agentic models. Collaborate with product and engineering, mentor junior scientists, implement evaluation-driven development, and ensure production reliability, scalability, and observability.