Machine Learning Engineer • Intern

Who you are

You are currently enrolled in a Master's or PhD program in Computer Science, Machine Learning, Artificial Intelligence, or a related field. You have a strong foundation in machine learning concepts and are eager to apply your knowledge in a practical setting. Your experience with Python and data science tools makes you a great fit for this role. You are collaborative and enjoy working with cross-functional teams to translate business needs into machine learning solutions. You are also comfortable experimenting with new algorithms and techniques to improve user experience and operational efficiency.

Desirable

Experience with TensorFlow or PyTorch is a plus, as is familiarity with scalable ML pipelines and infrastructure. You have a keen interest in personalized recommendations and content optimization, and you are excited about the opportunity to contribute to Netflix's data-driven features.

What you'll do

As a Machine Learning Engineer Intern at Netflix, you will help design, build, and deploy machine learning models that power personalized recommendations and enhance streaming quality. You will collaborate with data scientists, product managers, and engineers to understand business needs and develop scalable ML solutions. Your role will involve maintaining ML pipelines and infrastructure, ensuring they are efficient and effective. You will also have the opportunity to experiment with new algorithms and techniques, contributing to the continuous improvement of user experience across the Netflix platform.

What we offer

Netflix offers a unique opportunity to work in a fast-paced environment where innovation is encouraged. You will gain hands-on experience in machine learning and data science, working alongside some of the best minds in the industry. The internship provides a chance to make a real impact on the Netflix platform, contributing to features that enhance user engagement and satisfaction. We value diversity and inclusion and encourage you to apply even if your experience doesn't match every requirement. This is a chance to grow your skills and advance your career in a leading tech company.