- Model evaluation, considering quality, latency, memory, and storage needs. You understand the best model for a task may not be the latest SOTA, but the one with the best trade-off.
- Strive to make SOTA models work efficiently on Apple platforms by converting them to native formats like Core ML or MLX, enabling execution on GPUs and the Neural Engine.
- Dive into large codebases, such as Transformers, to optimize model architectures for Apple Silicon platforms, debug issues, and develop workarounds.
- Write Swift code to implement or optimize ML tasks, including pre-and post-processing pipelines.
- Produce high-quality technical documentation, including blog posts, tutorials, guides, social media threads, and concise demo apps.
- Contribute to open source projects, like coremltools, to improve coverage of PyTorch operations.
- Create tools that enable developers to convert, run, and share models easily, making it straightforward for researchers and practitioners to distribute models in device-friendly formats.
- Occasionally, write or be ready to understand low-level code such as parallel GPU kernels.
- Experienced Swift Developer: Have a strong background in Swift development with a practical, builder mindset and a good sense of software and application design.
- Passionate About ML: Have a deep understanding of essential model architectures and a passion for machine learning.
- Core ML Proficiency: Have experience using Core ML and understand its advantages and limitations.
- Open Source Contributor: Are eager to publish and contribute to open-source libraries to help developers adopt ML.
- Versatile Engineer: Can move across different levels of abstraction as needed, from UI to Metal kernels.
- Readable Code: Write code that is easy to understand but are also prepared to make critical path ugly for optimization's sake. (But just the critical path, please
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On-device ML Engineer - New York, United States - Hugging Face
Description
Job Description
Job DescriptionHere at Hugging Face, we're on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better.
We have built the fastest-growing, open-source, library of pre-trained models in the world. With more than 1 Million+ models and 320K+ stars on GitHub, over companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA.
About the RoleAs an On-device ML Engineer, you will explore cutting edge methods to run models on consumer platforms, with a special focus on Apple technologies. Your responsibilities will include optimizing, quantizing, and converting the best models for efficient execution on iPhones and Macs. Additionally, you will design, build, and contribute to open source software that demonstrates model usage and develop libraries to minimize friction for developers who may not be deeply familiar with ML. Beyond the technical challenges, your goal will be to disseminate these methods, facilitate their adoption, and create tools for the community.
Day-to-day tasks may include the following:
You'll thrive in this position if you are:
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