Recommendation Algorithm Engineer
5 days ago
**About Us**:
We are a global dating app created to give everyone a chance at love. The sense of belonging and connectedness we get from relationships helps us survive and thrive. Inspired by the stories of users who’ve transformed their lives through our platform, we’re committed to innovating meaningful connections—and we’d love for you to grow with us
**The Opportunity**:
This role is based in Singapore and reports to the Head of Machine Learning.
**Key Responsibilities**:
**Algorithm Development & Optimization**:
- Design and implement recommendation algorithms tailored to social matchmaking and discovery.
- Work across the full recommendation stack—from recall to ranking to engagement optimization.
- Continuously evaluate and improve model performance using online and offline metrics.
**Machine Learning & Deep Learning**:
- Build advanced behavioral and contextual features to power more nuanced recommendations.
- Train and deploy models at scale, optimizing for both relevance and system efficiency.
**User Profiling & Personalization**:
- Build rich user profiles by analyzing behavioral signals and interaction data.
- Develop systems that uncover evolving user interests and adapt recommendations in real time.
- Drive improvements in match quality, engagement, and long-term user satisfaction.
**Collaboration & Impact**:
- Partner with product, engineering, and data teams to align algorithms with product goals.
- Share insights through model explainability, dashboards, and documentation.
- Participate in model reviews and experimentation to guide product decisions.
**Qualifications**:
**Must-Haves**:
- Master’s or PhD in Computer Science, Machine Learning, or related fields.
- 3+ years of experience working on recommendation systems or large-scale machine learning models.
- Proficient in Python and ML frameworks (e.g., TensorFlow, PyTorch).
- Deep understanding of ranking, recall, and conversion models.
- Experience with feature engineering and user behavior modeling.
**Nice-to-Haves**:
- Familiarity with graph neural networks or reinforcement learning in recommender systems.
- Experience with distributed training and inference pipelines.
- Strong math/stats foundation (e.g., multi-objective optimization, Bayesian methods).
- Prior experience in social platforms, dating apps, or user-generated content.
**Why Join Us?**:
- **Cutting-Edge Work**: Tackle complex algorithmic problems that directly shape user experience.
- **Real-World Impact**: Your models influence how millions connect, interact, and form meaningful relationships.
- **Collaboration**: Join a cross-functional team that values experimentation, empathy, and speed.
- **Growth**: Develop at the intersection of AI, social systems, and human behavior.
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