Building general purpose robotic intelligence. Backed by General Catalyst, Lightspeed and Sequoia.
About the role
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.
What they're looking for
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience
- Minimum of 3 years of industry experience
- Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc
- Strong background in distributed computing, parallel processing techniques, handling large-scale datasets and data preprocessing
- Deep understanding of state-of-the-art machine learning techniques and models
- Experience with cloud-based training environments (AWS, Google Cloud, Azure)
More about this role
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 Software Engineer to work at the forefront of developing and optimizing the software infrastructure and tools necessary for training cutting-edge AI models. You will focus on building robust, scalable, and efficient training pipelines and frameworks that support the entire machine learning lifecycle, from data preparation to model deployment. You will collaborate with researchers and machine learning engineers to ensure seamless integration and operation of training systems, pushing the boundaries of...
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