Top Machine Learning Jobs in Raleigh, NC

The Sr. Application Administrator is responsible for the administration and support of data applications, serving as a Subject Matter Expert. Responsibilities include troubleshooting issues, architecting applications to meet business needs, monitoring application performance, and implementing automation solutions. The role also involves analyzing processes for improvements and maintaining vendor relationships to ensure application functionality.

As a Lead AI ML Engineer, you will design, develop, and deploy machine learning models and data pipelines. Responsibilities include managing data infrastructure, collaborating with stakeholders, and ensuring high-quality AI/ML project delivery through strong data analysis and visualization skills.

As a Machine Learning Engineer at Hugging Face, you will integrate machine learning models with advanced hardware, ensure high performance and develop Python APIs. You will work closely with AI technologies, focusing on low-level systems and compiler optimizations to enhance efficiency in model training and deployment.

The Staff Product Manager for AI Safety will develop and execute strategies for AI capabilities, collaborate with various teams to create a roadmap addressing regulatory needs and safety systems. This role involves applying emerging technologies and strong analytical skills to enhance product development while engaging with stakeholders.

As a Machine Learning Engineer at Hugging Face, you will develop specialized ML software, utilize existing libraries for scalable solutions, enhance software reliability, and manage production environments. Collaboration with the team to drive innovation and optimize performance is key.

The Cloud Machine Learning Engineer at Hugging Face will develop machine learning solutions leveraging cloud technologies, integrate open-source libraries with major cloud platforms, ensure model performance, and create developer experiences and APIs. Responsibilities also include writing technical documentation and engaging with the community.

The internship focuses on expanding the Hugging Face ecosystem by creating and maintaining JS/TS machine learning libraries. Responsibilities include optimizing models for in-browser inference, enabling models to run at near-native speeds, building demo applications, and fostering an open-source community.

The Machine Learning Engineer Internship at Hugging Face focuses on expanding AI tools used in Gradio, a library for machine learning demos. Interns will assist in creating a model pipeline leveraging existing Gradio spaces and prompts. Ideal candidates are passionate about open-source and have experience with modern deep learning libraries and LLMs, with a focus on user-friendly product development.