We are seeking a highly motivated PhD Research Scientist Intern to join our robotics teams at Amazon. This internship offers a unique opportunity to work on cutting-edge robotics projects that directly impact millions of customers worldwide. You will collaborate with world-class experts, tackle groundbreaking research problems, and contribute to the development of innovative solutions that shape the future of robotics and artificial intelligence. As a Research Scientist intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using modeling software and programming techniques for complex problems, implement prototypes, and work with massive datasets. You'll find yourself at the forefront of innovation, working with large language models, multi-modal models, and modern reinforcement learning techniques, especially as applied to real-world robots. Imagine waking up each morning, fueled by the excitement of solving intricate puzzles that have a direct impact on Amazon's operational excellence. Your day might begin by collaborating with cross-functional teams, exchanging ideas and insights to develop innovative solutions in robotics and AI. You'll then immerse yourself in a world of data and algorithms, leveraging your expertise in large language models and multi-modal systems to uncover hidden patterns and drive operational efficiencies. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Research Scientist Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA, and San Francisco, CA. We are particularly interested in candidates with expertise in: Robotics, Computer Vision, Artificial Intelligence, Causal Inference, Time Series, Large Language Models, Multi-Modal Models, and Reinforcement Learning. In this role, you gain hands-on experience in applying cutting-edge analytical and AI techniques to tackle complex business challenges at scale. If you are passionate about using data-driven insights and advanced AI models to drive operational excellence in robotics, we encourage you to apply. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and have the ability to thrive in a fast-paced, ever-changing environment. A day in the life Work alongside global experts to develop and implement novel scalable algorithms in robotics, incorporating large language models and multi-modal systems. Develop modeling techniques that advance the state-of-the-art in areas of robotics, particularly focusing on modern reinforcement learning for real-world robotic applications. Anticipate technological advances and work with leading-edge technology in AI and robotics. Collaborate with Amazon scientists and cross-functional teams to develop and deploy cutting-edge robotics solutions into production, leveraging the latest in language models and multi-modal AI. Contribute to technical white papers, create technical roadmaps, and drive production-level projects that support Amazon Science in the intersection of robotics and advanced AI. Embrace ambiguity, maintain strong attention to detail, and thrive in a fast-paced, ever-changing environment at the forefront of AI and robotics research.
November 18, 2024Large language models’ emergent abilities are improving with scale; as scale grows, where are LLMs heading? Insights from Ray Solomonoff’s theory of induction and stochastic realization theory may help us envision — and guide — the limits of scaling.
