We are seeking an experienced Software Engineer with a strong background in reinforcement learning (RL) and controls for autonomous vehicles. This role involves the design, development, and deployment of advanced control algorithms and RL techniques for maneuvering and motion in space, with an expected background in some mobile platforms, including drones, ground vehicles, marine vehicles, or spacecraft. The ideal candidate will have extensive experience in developing and implementing deep neural networks and reinforcement learning algorithms, along with a solid understanding of control systems in dynamic environments as well as understanding of the limitations and benefits of these techniques.
Key Responsibilities:
- Design, develop, and implement reinforcement learning algorithms for autonomous control and maneuvering of vehicles.
- Develop and deploy deep neural networks for perception, decision-making, and control tasks.
- Integrate RL models with vehicle control systems to optimize performance in complex environments.
- Conduct simulations and real-world testing to validate control algorithms and RL models.
- Collaborate with cross-functional teams to define requirements and specifications for autonomy and control systems.
- Develop and maintain software in C++ and Python, utilizing relevant libraries and frameworks for RL and deep learning.
- Optimize code for performance and scalability on various platforms, including embedded systems.
- Stay updated with the latest research and advancements in reinforcement learning, deep learning, and control systems.
- Document software designs, algorithms, and development processes.
Required Qualifications: (U.S. Citizenship Required)
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related field. PhD is a plus.
- 5-10 years of professional experience in software engineering, with a focus on reinforcement learning and control systems.
- Extensive experience with deep neural networks and reinforcement learning frameworks (e.g., TensorFlow, PyTorch, Keras).
- Strong proficiency in C++ and Python, along with relevant libraries and tools for RL development.
- Demonstrated experience in the design and implementation of control systems for autonomous vehicles in air, ground, sea, or space environments.
- Solid understanding of vehicle dynamics, motion planning, and control theory.
- Proven ability to develop and optimize algorithms for real-time applications.
- Experience with simulation tools and environments (e.g., Gazebo, ROS, MATLAB/Simulink).
- Strong analytical and problem-solving skills, with a keen attention to detail.
- Excellent communication and collaboration skills.
Preferred Qualifications:
- Experience with embedded systems and real-time operating systems (RTOS).
- Familiarity with hardware integration and sensor fusion techniques.
- Knowledge of safety and reliability considerations for autonomous systems.
- Experience with version control systems (e.g., Git) and software development methodologies (e.g., Agile).