Machine Learning Engineer Intern
Hinge
Software Engineering
Palo Alto, CA, USA
USD 47-47 / hour
Take the Lead
We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down.
Move Fast
We have a bias for action and urgency. Something that could be done tomorrow would be better if done today.
Better Together
We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate.
Real Talk
We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same.
Safety First
We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us.
Spark Fun
We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters.
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.
47 - 47 USD