Machine Learning Engineer Intern

Location: Hybrid in Palo Alto, California, with in-office collaboration required three days per week

Salary: USD 47 per hour

Company

hirify.global’s Tinder engineering organization builds and operates a large-scale dating platform focused on human connection, user experience, trust and safety, and business growth.

What you will do

• Design and implement machine learning algorithms for Recommendation, Trust, Profile, Chat, Growth, or Revenue domains.
• Formulate machine learning models for production problems under guidance.
• Build and deploy machine learning solutions with senior engineers to advance Tinder’s business goals.
• Work on challenges that improve the user experience at Tinder’s global scale.
• Participate in the annual Tindership Hackathon and present the team’s work to Tinder employees and executives.

Requirements

• Currently pursuing a BS/BA or MS in Computer Science or a related field.
• Practical knowledge of building efficient end-to-end machine learning workflows.
• Proficiency in Python.
• Hands-on experience designing and building machine learning models.
• Foundational knowledge of computer science principles, including data structures and algorithms.
• Interest in working on large-scale challenges with cutting-edge technology.

Nice to have

• Publications in top machine learning or data science conferences such as NeurIPS, ICML, RecSys, or KDD.
• Experience deploying machine learning 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 such as Go, Java, or Scala.

Culture & Benefits

• Internship program runs from June 1 through August 31, 2027.
• Hybrid work with three required in-office collaboration days per week in Palo Alto.
• Values include taking ownership, moving quickly, collaboration, candid communication, safety, and creativity.
• Inclusive workplace committed to equal opportunity and accommodating applicants who require reasonable support during the application or interview process.