Summary
What you’ll impact
The Machine Learning Engineer at the organization will design and implement advanced reinforcement learning algorithms for robotic applications, conduct experiments, and collaborate with research and engineering teams to integrate models into real-world robots. This role focuses on optimizing model performance, staying current with RL research, and contributing to the development of adaptable, autonomous robotic systems.
Responsibilities
What you'll do
- Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
- Design and conduct experiments to train RL models and conduct real-world tests.
- Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
- Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
- Analyze and interpret experimental results, iterating on model design to achieve desired performance.
- Stay up-to-date with the latest research and advancements in reinforcement learning.
Requirements
What you’ll bring
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
- Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
- Strong background in algorithms, data structures, and software engineering principles.
- Experience with physics simulation engines and tools for training RL.
- Deep understanding of state-of-the-art machine learning techniques and models.
- Extensive industry experience with reinforcement learning and robotic systems.