AI Training Infrastructure Developer
6 days ago
At Skild AI, we're revolutionizing the field of artificial intelligence by creating a robust and adaptable robotic intelligence that can thrive in unpredictable environments. Our team is comprised of individuals with diverse backgrounds and experiences, from recent graduates to domain experts. While relevant industry experience is valuable, we place greater emphasis on demonstrated abilities and attitude.
Job OverviewWe're seeking an AI Training Infrastructure Developer to spearhead the development and optimization of software infrastructure and tools necessary for training cutting-edge AI models. You will be responsible for crafting robust, scalable, and efficient training pipelines and frameworks that support the entire machine learning lifecycle, from data preparation to model deployment. Collaborating closely with researchers and machine learning engineers, you'll ensure seamless integration and operation of training systems, pushing the boundaries of what AI can achieve in real-world robotics applications.
Main Responsibilities- Design and maintain robust, scalable, and distributed training pipelines (data preprocessing, training orchestration, and model evaluation) and frameworks for large-scale AI models.
- Optimize training processes for performance and resource utilization, ensuring scalability and reliability.
- Work with researchers and machine learning engineers to integrate state-of-the-art algorithms and techniques into training pipelines.
- Monitor and analyze training, identifying bottlenecks and proposing solutions to improve efficiency and performance.
- Ensure the robustness and reliability of the training infrastructure, including automated testing and continuous integration.
- Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field, or equivalent practical experience.
- Proficiency in Python, C++, or similar languages 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).
- Experience in developing and maintaining software tooling and infrastructure for machine learning.
- Deep understanding and practical experience with software engineering principles, including algorithms, data structures, and system design.
- Experience with continuous integration and automated testing frameworks.
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