About the job
Intrinsic is a pioneering initiative under Alphabet that seeks to redefine the landscape of industrial robotics. Our team is driven by the conviction that advancements in artificial intelligence, perception, and simulation will revolutionize industrial robotics in the near future, with data and software at the forefront.
Our mission is to make industrial robotics more intelligent, accessible, and user-friendly for an extensive range of businesses, entrepreneurs, and developers. We are a vibrant collective of engineers, roboticists, designers, and technologists committed to unlocking the creative and economic potential of industrial robotics.
Role
As a Software Engineering Intern in AI-Driven Robotic Manipulation Research, you will explore cutting-edge robotics capabilities that significantly impact real-world manufacturing applications. You will collaborate with a team of researchers and engineers to develop features and conduct experiments utilizing machine learning techniques for robot manipulation.
You should possess a comprehensive understanding of robot learning, a passion for executing robotic hardware experiments, and strong programming skills.
How Your Contributions Propel Our Mission
- Assist in experimentation with innovative, AI-enabled, and sensor-guided manipulation capabilities.
- Assess new prototypes in manipulation scenarios for industrial manufacturing.
- Enhance existing manipulation capabilities within our technology stack and refine features related to learning and robot control.
- Collaborate with researchers and colleagues across three continents to deliver real-world solutions.
Skills Required for Success
- PhD student in Computer Science, Robotics, or a related field, or a Master's student with hands-on robotics lab experience.
- Proficiency in programming with Python (ideally with Jax or PyTorch).
- Knowledge and initial experience in machine learning applications for robotics, including reinforcement learning, vision-language-action models, sim-to-real transfer learning, imitation learning, or foundational models for robotics.
- A genuine enthusiasm for experimenting with robot hardware, with previous experience being a plus.
- Fundamental understanding of robot motion control, kinematics, and dynamics.
- Experience in controlling robot motion and/or implementing learned models in motion is preferred.

