BS, MS or higher degree in Computer Science, Robotics, Engineering, or related field, or equivalent experience.
Proficiency in Python, C++, or similar programming languages, along with a deep learning framework such as PyTorch, TensorFlow, or JAX.
In-depth knowledge of various reinforcement learning algorithms (model-free, model-based, etc.).
Strong foundation in algorithms, data structures, and software engineering principles.
Experience with physics simulation engines for training reinforcement learning models.
Deep understanding of cutting-edge machine learning techniques.
Extensive industry experience in reinforcement learning and robotic systems.
Responsibilities
Develop and implement state-of-the-art reinforcement learning algorithms for robotics.
Design and conduct experiments to train RL models and perform real-world testing.
Collaborate with researchers to explore novel scaling methods for RL model training.
Communicate with deployment engineers to integrate RL models into robotic systems effectively.
Analyze experimental results and iterate on model design for performance improvement.
Stay updated on the latest research in reinforcement learning.
Benefits
Work in a dynamic team with a diverse range of experience levels.
Engage in cutting-edge projects that push the boundaries of robotic intelligence.
Opportunity to directly impact the development of autonomous robots.
Collaborative work environment that emphasizes communication across disciplines.
Full Job Description
Position Overview
We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously. Responsibilities
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.
Preferred Qualifications
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.
