Machine Learning Engineer, General Intelligence Specialist
3 weeks ago
We are seeking a highly skilled Machine Learning Engineer to join our Amazon General Intelligence team. As a key member of our team, you will play a pivotal role in the development of industry-leading multi-modal and multi-lingual Large Language Models (LLMs).
Key Responsibilities- Algorithm Development: Design and develop novel algorithms and modeling techniques to advance the state of the art of large model training using cutting-edge hardware like NVIDIA GPUs.
- Platform Development: Responsible for the development and maintenance of key platforms needed for developing, evaluating, and deploying LLMs for real-world applications.
- Collaboration: Work closely with other team members to investigate design approaches, prototype new technology, and evaluate technical feasibility.
- Data Processing: Work with applied scientists to process massive data, scale machine learning models, and optimize performance.
Our team is at the forefront of AI research and development, pushing the boundaries of deep learning, reinforcement learning, and more. As a member of our team, you will have the opportunity to collaborate with top minds, gain valuable experience, and accelerate your career growth.
Requirements- Professional Experience: 3+ years of non-internship professional software development experience.
- Design and Architecture: 2+ years of non-internship design or architecture experience, including design patterns, reliability, and scaling.
- Programming Skills: Experience programming with at least one software programming language.
- Full Software Development Life Cycle: 3+ years of experience in the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
- Master's Degree: Master's degree in computer science or equivalent.
- Advanced Techniques: Experience in techniques like kernel fusion and custom kernels to improve GPU utilization, mixed precision training using lower precision and dynamic loss scaling, and/or demonstrated ability to implement efficient memory management.
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