Match Group
From Match Group's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Where you’ll work This is a hybrid role that requires in-office collaboration three days per week in Palo Alto, California.
Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more.
The Tinder ML team drives impact across nearly every core domain of the product — from Recommendations and Trust & Safety to Profile, Chat, Growth, and Revenue. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem. With Tinder's global scale and impact, you'll be at the forefront of solving some of the most complex challenges in technology. In this internship program, you will collaborate with senior engineers to build and deploy ML solutions that advance Tinder’s business goals.
Gain real-world experience on exciting challenges to improve the user experience.
Design and implement machine learning algorithms for the assigned domains: Recommendation, Trust, Profile, Chat, Growth, or Revenue.
Experience the ML model formulation of production problem under the guidance.
Work closely with a Senior Engineers to develop machine learning solutions to further Tinder’s business goals.
Practical knowledge of how to build efficient end-to-end ML workflows.
Proficient in Python.
Hands-on experience in designing and building ML models.
Foundational knowledge of basic Computer Science principles: data structures and algorithms.
Experience deploying ML models in production environments.
Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or Keras.
Proficiency in additional programming languages like Go, Java or Scala.
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