Skild AI
Machine Learning Engineer
San Mateo, California, USA · full-time
Sponsorship not specified$100k-$300kDetected 42 days ago
PythonC++Data StructuresAlgorithmsMachine LearningDeep LearningTensorFlowPyTorchRoboticsResearchCollaborationAdaptability
About the role
- 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.
- This will require close collaboration with our robotics, research, and engineering team.
Responsibilities
- Company Overview At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing.
- Design and conduct experiments to train RL models and conduct real-world tests.
- Analyze and interpret experimental results, iterating on model design to achieve desired performance.
Requirements
- Preferred Qualifications BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
Nice to have
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Strong background in algorithms, data structures, and software engineering principles.
- Experience with physics simulation engines and tools for training RL.
Compensation
- Range $100,000-$300,000 USD
Benefits
- 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.
- Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
- Stay up-to-date with the latest research and advancements in reinforcement learning.
- 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.).
- Deep understanding of state-of-the-art machine learning techniques and models.
- Extensive industry experience with reinforcement learning and robotic systems.
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