Physical AI Software Co-Op/Intern

Luminous; Boston (MA); In loco; USD 28.000 - 55.000; Part-time

Vantaggi offerti da questo lavoro

Mentorship from senior ML and robotics

Ownership of models and pipelines

Startup environment exposure

Hands-on robotics experience

Team off-sites and events

Descrizione del lavoro

Luminous Robotics, Inc. is seeking a Physical AI Intern/Co-op to advance the perception and learning-based autonomy stack for our LUMI robots. You will evaluate state-of-the-art models, integrate them into the autonomy stack, and help scale MLOps for training pipelines on real solar construction sites.

The role emphasizes hands-on implementation on robots in the field, with mentorship from senior ML and robotics engineers, and a path toward impact on deployed systems. Spring 2027 start.

Competenze

• PhD or advanced MS in CS or a related field.
• Deep reinforcement learning (DRL) and imitation learning (IL) expertise with state-of-the-art algorithms.
• Must have implemented at least one DRL algorithm and at least one robotics algorithm on a real robot.
• Hands-on experience with C++, Python, ROS, and Docker.
• Familiar with AI coding assistants and developer tools to accelerate development.
• Interest in robotics, renewable energy, or clean tech.
• Experience with Git, Agile methods, Jira or equivalent.
• Excellent communication and organizational skills.
• Team-oriented and able to thrive in a fast-paced startup environment.
• Bonus: published a paper at major venues; cloud infrastructure experience.

Mansioni

• Contribute to perception and learning-based autonomy components of the LUMI robotics stack.
• Evaluate and integrate state-of-the-art ML architectures into the autonomy stack.
• Help expand MLOps infrastructure: training pipelines, evaluating architectures, integration with annotation tools.
• Write production code with rigorous coding practices; participate in code reviews and docs.
• Analyze sensor data and build visualization tools to evaluate model performance in real and simulated environments.
• Support experiments benchmarking learned policies against classical approaches in real field conditions.

Conoscenze

C++

Python

ROS

Docker

DRL

Imitation learning

Git

Agile methods

Jira

Communication

Formazione

PhD or advanced MS in Computer Science or related field

Strumenti

ROS

Docker

Descrizione del lavoro

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.

Role Description

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. Starting in Spring 2027.

Responsibilities

• 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 deeply curious about ML and robotics, but grounded in what it takes to make a model actually work on a physical robot outdoors, not just in a notebook. We look for people eager to learn fast, take ownership early, and contribute to accelerating the world's transition to sustainable energy.

• Competitive hourly/stipend compensation
• Direct mentorship from senior ML and robotics engineers
• Real ownership of models and pipelines that ship to deployed robots
• Exposure to a high-growth startup environment
• Hands-on experience with cutting-edge robotic systems
• Access to team off-sites, workshops, and events