Machine Learning Engineer, Reinforcement Learning
Pittsburgh, San Mateo
Posted 5mo ago
Job Location
Pittsburgh, San Mateo
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
Skild AI is building the world's first general-purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We are seeking a Machine Learning Engineer to design and implement cutting-edge reinforcement learning algorithms for robotic applications. This role involves conducting experiments, optimizing models for real-world robotic environments, and collaborating closely with our robotics, research, and engineering teams. Your work will directly contribute to the development of intelligent, adaptable robots capable of autonomous learning and complex task performance.
Responsibilities
- Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
- Design and conduct experiments to train RL models and perform real-world tests.
- Collaborate with researchers to explore novel methods for scaling reinforcement learning model training.
- Communicate with engineers to integrate RL models into robotic systems and iterate on deployment methods.
- Analyze and interpret experimental results, iterating on model design for desired performance.
- Stay current with the latest research and advancements in reinforcement learning.
Requirements
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Proficiency in Python, C++, or similar.
- Proficiency in 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.