BS, MS or higher in Computer Science, Robotics, Engineering, or related field, or equivalent experience.
Proficiency in Python, C++, or similar, with at least one deep learning library (PyTorch, TensorFlow, JAX).
Deep understanding of reinforcement learning algorithms (model-free, model-based, etc.).
Strong background in algorithms, data structures, and software engineering.
Experience with physics simulation tools for training reinforcement learning models.
Familiarity with state-of-the-art machine learning techniques and models.
Responsibilities
Develop and implement advanced reinforcement learning algorithms for robotic applications.
Design and conduct experiments to train and validate RL models in real-world scenarios.
Collaborate with researchers to enhance reinforcement learning model training techniques.
Communicate with engineers to integrate RL models into robotic systems.
Analyze experimental results and iterate on model design for performance improvement.
Stay updated with the latest advancements in reinforcement learning.
Benefits
Flexible working arrangements that support work-life balance.
Opportunities for professional development and continuing education.
Collaborative work environment with a diverse, passionate team.
Access to the latest technologies and resources in robotics and AI.
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.
