Machine Learning Engineer, Reinforcement Learning

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

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.

• →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.

• 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.