Machine Learning Engineer Intern

Program Duration

The internship program will run from June 1 through August 31, 2027.

Where you’ll work

This is a hybrid role that requires in-office collaboration three days per week in Palo Alto, California.

About the Role

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.

What you'll do:

• 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.
• Participate in the annual Tindership Hackathon, presenting with your team to the entire Tinder team and panel of executives.

What we're looking for:

• Aspiring ML Engineer who’s excited to work on large scale challenges with cutting-edge technology.
• 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.
• Currently pursuing a BS/BA or MS in Computer Science or a related field.

Nice to have:

• Publications in top ML or data science conferences (e.g., NeurIPS, ICML, RecSys, KDD).
• Experience deploying ML models in production environments.
• Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or Keras.
• Experience with Databricks, Spark, or Airflow.
• Proficiency in additional programming languages like Go, Java or Scala.