Job Description
Title: Machine Learning Engineer, Fast Optimized Inference – US Remote
Location: Remote Remote US
JobDescription:
Here 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 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Algolia, Grammarly and NASA.
About the role:
As a Machine learning Engineer, you work mainly on creating great libraries highly focused on real world ML use cases. We’re building on top of our open-source to create more specialized code with a focus on industrial level of usage.
We are searching for someone who brings fresh ideas, demonstrates a unique and informed viewpoint, and enjoys collaborating with a progressive, nimble and decentralized approach to develop real-world solutions and positive user experiences at every interaction.
Objectives of this role:
- Develop specialized software for specific machine learning (ML) use cases that have broad applications, similar to [text-generation-inference]
- Utilize existing library frameworks to create scalable software solutions for industrial purposes.
- Enhance the reliability, quality, and time-to-market of our software suite. Measure and optimize system performance to stay ahead of customer needs and drive innovation.
- Manage the production environment by monitoring availability and ensuring overall system health. We run our own tools
About you:
If you are a passionate Machine Learning Engineer with a keen interest in AI and proficient with Python, Rust and specialized Cuda kernels Frameworks (transformers of course + Keras or PyTorch), we would love to hear from you. Join our team and contribute to the advancement of AI technologies while working alongside talented professionals in a collaborative and stimulating environment.