Deep Learning Intern Jobs in Central, SC (NOW HIRING)

As of Aug 6, 2026, the average hourly pay for deep learning intern in Central, SC is $14.58, according to ZipRecruiter salary data. Most workers in this role earn between $12.36 and $16.44 per hour, depending on experience, location, and employer.

As a Deep Learning Intern, you can expect to work on projects such as developing and training neural network models, data preprocessing, and conducting experiments to improve model accuracy. Interns are often integrated into small teams where they collaborate closely with experienced machine learning engineers and researchers. Mentorship is usually structured through regular check-ins, code reviews, and collaborative problem-solving sessions, giving interns the opportunity to learn industry best practices and receive feedback on their work. This setup provides a supportive environment for skill development and hands-on experience with real-world deep learning challenges.

To thrive as a Deep Learning Intern, you need a solid background in mathematics, programming (especially Python), and foundational knowledge of machine learning concepts, often backed by coursework or relevant projects. Familiarity with frameworks like TensorFlow or PyTorch, as well as experience using version control systems like Git, are typically required. Strong problem-solving abilities, curiosity, and effective communication skills help interns collaborate and learn quickly in a dynamic research environment. These skills and qualities are essential for contributing meaningfully to cutting-edge AI projects and rapidly adapting to evolving technologies.

A Deep Learning Intern typically assists with designing, developing, and testing deep learning models under the supervision of experienced machine learning engineers or researchers. Their tasks may involve data preprocessing, model training, evaluation, and implementing neural network architectures for tasks like image recognition, natural language processing, or other AI applications. Interns often help with literature reviews, experiment tracking, and preparing reports or presentations of their findings. This role provides hands-on experience in working with state-of-the-art machine learning frameworks such as TensorFlow or PyTorch.