Verus Research is searching for a Machine Learning Engineer – Robotics to perform research & development, conception, and implementation of advanced navigation, control, and other complex tasks for mobile robotics and autonomous system development. The candidate should have experience developing machine learning algorithms with a focus on some combination of computer vision, reinforcement learning, and guidance, navigation, and control applications.
The primary role for the Machine Learning Engineer – Robotics will be to aid in the development of novel machine learning algorithms with application to robotic systems, and the subsequent testing and deployment of those algorithms both on simulated systems and in a hardware laboratory. One of the main applications of this work is to introduce more autonomy into spacecraft. The work will require strong research and critical thinking skills and the ability to stay up to date on the latest developments in robotics applications of machine learning.
The ideal candidate for the Machine Learning Engineer – Robotics position will apply strong analytical skills to solve challenging problems in a fast-paced environment to advance capabilities for autonomous systems, including spacecraft. In addition, the ideal candidate will possess the following:
- U.S. Citizenship
- Currently holding or being able to obtain a Department of Defense security clearance
- Advanced degree in computer science, mathematics, electrical engineering, aerospace engineering, or related fields.
- At least 3 years’ experience working on machine learning problems
- Demonstrated successful application of machine learning to robotics applications through prior experience and/or peer-reviewed publications
- Research experience in computer vision and/or reinforcement learning
- Proficient in Python (C++ is highly desired)
- Experience with TensorFlow or other machine learning frameworks
- Ability to work independently and on a team and be able to communicate technical concepts clearly and concisely, both verbally and in writing