AI Engineer - Machine Learning 2

Business Integra - Redmond, WA

Full-time - Mid Level

Redmond, WA

Professional, Scientific, and Technical Services

About the position

The AI Engineer - Machine Learning 2 role is focused on supporting an AI research team dedicated to teaching robots new skills. This position involves collaboration with software and hardware engineers to integrate various technologies and enhance the capabilities of robotic systems. The engineer will work on machine learning models, contribute to the development of training systems, and ensure the efficiency and reliability of the training infrastructure.

Responsibilities

• Create and uphold efficient, scalable, and distributed training systems including data preprocessing, training orchestration, and model assessment for training large-scale AI models.
• Enhance the efficiency of training procedures to improve performance and use of resources, while maintaining scalability and dependability.
• Collaborate with researchers to create training and evaluation pipelines for state-of-the-art algorithms.
• Develop and design benchmarks for evaluating ML models.
• Perform training and fine-tuning of foundation models for robotic applications.
• Monitor and analyze pipelines, 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

• Bachelor's degree in computer science or related field is required.
• 2+ years experience with Linux.
• 2+ years experience with Python.
• 2+ years experience with Configuration management.
• 2+ years experience with ROS/ROS2.
• 2+ years experience with TensorFlow.
• 2+ years experience with any sort of machine learning packages.

Nice-to-haves

• Master's degree or higher in Computer Science, Robotics, Engineering or a related field.
• Proficiency in C++ or similar languages.
• Experience with cloud-based training environments (AWS, Google Cloud, Azure).
• Experience in developing and maintaining software tooling and infrastructure for machine learning.
• Deep understanding of state-of-the-art machine learning techniques and models.