Intrinsic aims to reimagine the potential of industrial robotics by making them intelligent, accessible, and usable for millions more businesses, entrepreneurs, and developers through advances in AI, perception, and simulation.
Requirements
- Be knowledgeable and comfortable in working with physical simulations (e.g., Gazebo, Bullet, MuJoCo, IsaacSim, etc.) to implement motion planning/machine learning algorithms.
- At least 2 years of experience in one or more of: motion planning for robotics, classical robot motion planning (e.g., RRTs, PRMs), machine learning/deep learning
- Solid Experience programming in C++, Python, and ideally Jax.
- Must have research experience in a related field and at least one high-quality publication in a peer-reviewed academic journal/conference
- Experience with the design and implementation of large, complex machine learning algorithms with large amounts of data and highly parallelized algorithms.
- 4 years of applied experience in machine learning for robotics motion planning.
Responsibilities
- Develop new motion planning approaches for single and multi-robot planning, starting from existing prototypes.
- Exploit advances in machine learning (e.g,. graph nets, deep-learning, transformer models, etc.) for developing novel data-driven motion planning systems.
- Implement state of the art algorithms and libraries in Python, C++, and Jax.
- Bring problems from industrial partners to functional new solutions with very high reliability and performance.
- Distill recent robotics/ML research publications for target problems and implement promising solutions.
Other
- Masters in Computer Science, Robotics or equivalent with practical experience in robotics and machine learning.
- PhD in Robotics, Computer Science or a similar technical field.
