Tinder is looking to solve the problem of personalizing user experiences to maximize engagement and retention by leveraging machine learning. The goal is to connect members with the right product experiences at the right time through intelligent notifications, personalized CRM campaigns, and optimized lifecycle journeys.
Requirements
- 2+ years of hands-on machine learning experience, with a track record of delivering impactful solutions at scale.
- Strong expertise in deep learning, ranking/retrieval models, or reinforcement learning.
- Proficiency in ML frameworks such as PyTorch or TensorFlow.
- Proficiency in Python, Java, Scala, or similar programming languages.
- Hands-on experience in engagement, growth, or lifecycle personalization domains.
- Demonstrated experience designing causal inference driven systems to optimize interventions.
- Experience building and serving large-scale ML systems with low-latency requirements.
Responsibilities
- Drive ML modeling efforts for Tinder’s engagement and growth experiences.
- Personalize notifications, CRM campaigns, and lifecycle journeys to improve member experience and retention.
- Apply advanced ML methods, including deep learning, reinforcement learning, ranking models and causal inference.
- Develop large-scale ML systems that serve recommendations with low latency and high reliability.
- Collaborate with product managers, ML engineers, and backend engineers to integrate models into user-facing experiences.
- Work with big data pipelines to improve the accuracy, efficiency, and long-term business impact of ML models.
Other
- This is a hybrid role and requires in-office collaboration three times per week.
- This position is located in Palo Alto, CA.
- BS, MS or PhD in Computer Science, Statistics, or a related field.
- A strong publication record at top ML conferences (e.g., NeurIPS, ICML, KDD, RecSys).
- Even if you don’t meet all the listed qualifications, we invite you to apply and show us how your skills could transfer.
