Software Engineer Intern

Software Co-op/Intern, Physical AI — Luminous 0 Skip to Content Home Press Media Progress Resources ARENA Culture Careers Contact Open Menu Close Menu Home Press Media Progress Resources ARENA Culture Careers Contact Open Menu Close Menu Home Folder: Press Back Media Progress Folder: Resources Back ARENA Culture Careers Contact Software Co-op/Intern, Physical AI South Boston, MA United Staes $20-25 hourly ABOUT LUMINOUS Luminous Robotics, Inc. is solving one of the defining infrastructure problems of this decade: decarbonization and AI data center growth are forcing the world to scale energy infrastructure faster than at any point in history. Based in Boston, we address this through flexible, physical AI robot fleets that build and operate solar energy infrastructure. In the past 12 months, we've developed three generations of LUMI robots and brought online clean energy for thousands of U.S. homes. We're now scaling into large-scale commercial fleet deployments globally THE ROLE We're looking for a Physical AI Intern/Co-op to push the learning-based edge of the LUMI autonomy stack — perception, deep RL, and the ML architectures that let our robots handle the unpredictability of real solar construction sites. You'll evaluate and integrate state-of-the-art models, and help build the infrastructure that lets our whole team iterate on them faster. This isn't research for its own sake — everything you build is aimed at what actually needs to work on a robot in the field. RESPONSIBLITIES Contribute to the perception and learning-based autonomy components of the LUMI robotics stack Evaluate and integrate state-of-the-art ML architectures, such as deep RL policies and VLAs, into LUMI's autonomy stack Help expand our MLOps infrastructure: building training pipelines, evaluating new architectures, and integrating with annotation tools Write production code following rigorous coding practices; participate in code reviews, documentation, and cross-functional engineering meetings Analyze sensor data and build visualization/debugging tools to evaluate model performance in real and simulated environments Support experiments benchmarking learned policies against classical approaches under real field conditions QUALIFICATIONS Current PhD (or advanced MS student) in Computer Science or a related field Excellent understanding of deep reinforcement learning (DRL) and imitation learning (IL), with the ability to explore state-of-the-art algorithms across the robotics stack Requirement: You must have implemented at least one DRL algorithm (beyond a class assignment), and you must have implemented at least one robotics algorithm on an actual robot (beyond a class assignment) Some hands-on experience with tools such as C++, Python, ROS, and Docker Familiarity with AI coding assistants such as Claude Code or OpenAI's developer tools, and comfort using them to accelerate development Demonstrated interest in robotics, renewable energy, or clean tech Git, Agile methods, Jira, or equivalent Excellent communication and organizational skills Ability to adapt in a fast-paced startup environment and willing to roll up sleeves, no matter the task Team-oriented and excited about real-world deployment challenges Bonus: published a paper at ICRA, IROS, AAAI, IJCAI, ICLR, NeurIPS, ICML, RLC, and so on Bonus: prior experience with perception algorithms or startup operations Bonus: experience with cloud infrastructure CULTURE FIT At Luminous, we value grit, creativity, and humility. The ideal candidate is hands-on, proactive, and excited to contribute in a fast-moving environment. We look for individuals who are eager to learn, unafraid to take ownership, and passionate about helping accelerate the world’s transition to sustainable energy. COMPENSATION & PERKS Exposure to a high-growth startup environment. Real ownership of production code that ships to deployed robots Direct mentorship from senior ML and robotics engineers Hands-on experience with cutting-edge robotic systems. Access to team off-sites, workshops, and events. Potential path to a full-time offer Apply Here