Principal Research Scientist, AI/ML

Our client, a renowned research institution, is seeking a highly innovative Principal Research Scientist specializing in Artificial Intelligence and Machine Learning for a fully remote position. While based within the research hub associated with **Austin, Texas, US**, this role allows for complete remote work, offering unparalleled flexibility. You will lead groundbreaking research initiatives, developing advanced AI/ML models and algorithms to solve complex scientific challenges. Your work will involve designing and implementing state-of-the-art machine learning systems, conducting rigorous experimentation, and publishing findings in top-tier academic journals and conferences. Key responsibilities include identifying novel research opportunities, defining research methodologies, and collaborating with a team of leading scientists and engineers. You will mentor junior researchers, contribute to intellectual property development, and stay at the forefront of AI/ML advancements. Expertise in areas such as deep learning, natural language processing, computer vision, reinforcement learning, or statistical modeling is essential. Proficiency in programming languages like Python and associated libraries (e.g., TensorFlow, PyTorch, Scikit-learn) is required. A Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a closely related quantitative field, with a strong publication record and demonstrated research leadership, is mandatory. A minimum of 8 years of post-doctoral research experience is expected. The ability to work autonomously, manage multiple research projects effectively, and communicate complex technical concepts clearly to both technical and non-technical audiences is crucial for this remote role. We are looking for a visionary scientist who can drive innovation, push the boundaries of AI/ML research, and contribute significantly to the scientific community. This is an exceptional opportunity to conduct high-impact research without geographical constraints, shaping the future of scientific discovery.