Software Engineer, AI Training and Infrastructure

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.

Requirements

• 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.

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.

Skills

• 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.

Pay

Base Salary Range: $100,000 USD - $300,000 USD