Software Engineer, AI Training and Infrastructure

Roles and Responsibilities:

-Develop 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. -Collaborate 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.

Preferred Qualifications:

-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). -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.