Software Engineer, AI Training and Infrastructure
San Mateo, CA
Posted 3mo ago
About the job
Skild AI is seeking a Software Engineer to develop and optimize the software infrastructure and tools for training cutting-edge AI models. This role involves building 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 integrate new algorithms and techniques, pushing the boundaries of AI in real-world robotics. The position also focuses on exploring efficient data utilization within training pipelines.
Responsibilities
- Develop and maintain scalable, distributed training pipelines and frameworks for large-scale AI models, including data preprocessing, training orchestration, and model evaluation.
- 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 to identify bottlenecks and propose solutions for improved efficiency and performance.
- Ensure the robustness and reliability of the training infrastructure through automated testing and continuous integration.
Requirements
- 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 languages.
- Experience with at least one deep learning library such as PyTorch, TensorFlow, or JAX.
- Strong background in distributed computing, parallel processing, 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.