Machine Learning Researcher

Company DescriptionGreat Sky is a venture-backed startup revolutionizing AI hardware by pushing the boundaries of physics. Through innovations like single-photon-level communication using light and superconducting electronics, Great Sky achieves unprecedented speed and energy efficiency in computing. By integrating processing and memory, the company significantly reduces data movement, providing transformative improvements in AI hardware capabilities. These breakthroughs enable multiple orders of magnitude in performance enhancements, unlocking AI’s full potential. Based in Boulder, CO, Great Sky is dedicated to redefining the technological frontier.Role DescriptionThis is a full-time hybrid role based in Boulder, CO, with the flexibility for partial work-from-home arrangements. As a Machine Learning Researcher, you will focus on designing and developing large scale, hardware aware, state space models for machine learning applications. Responsibilities include implementing data-driven models, conducting experiments, analyzing large datasets, and optimizing neural network architectures. You will collaborate closely with cross-functional teams to align advanced machine learning techniques with the company's cutting-edge hardware capabilities, addressing real-world challenges.Required:Graduate or post-graduate experience in computer science, machine learning, physics, or mathematicsTrack record of influential architectural or algorithmic contributions to the AI communityExperience with large-scale simulation workflows Proficiency with at least one modern deep-learning framework (PyTorch, TensorFlow, JAX, or equivalent)Strong mathematical foundations in linear algebra, probability, dynamical systems, optimization, and statisticsDeep familiarity with state-of-the-art literature in machine learning in general and RNNs in particularPreferred:Experience with hardware-aware algorithm co-design, especially for analog or in-memory platformsExperience in software development or AI applicationsExperience with computing clusters and training large-scale modelsLeadership or mentorship experienceComfortable presenting technical achievements to a variety of audiences