Recommendation Algorithm Engineer

Borr Drilling; Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

Join a dynamic team as a Recommendation Algorithm Engineer in Singapore, focusing on developing advanced machine learning models for personalized matchmaking experiences. Leverage your expertise in algorithm design and deep learning to enhance user interactions and contribute to real-world impact in a rapidly growing domain.

Benefits

Cutting-Edge Work

Real-World Impact

Opportunity for Growth

Collaborative Environment

Qualifications

• 3+ years of experience with recommendation systems or large-scale ML models.
• Deep understanding of ranking, recall, and conversion models.
• Familiarity with graph neural networks or reinforcement learning is a plus.

Responsibilities

• Design and implement recommendation algorithms tailored to social matchmaking.
• Continuously evaluate and improve model performance.
• Build rich user profiles by analyzing behavioral signals.

Skills

Python

Machine Learning

Recommendation Systems

Deep Learning

Feature Engineering

Education

Master's or PhD in Computer Science

Tools

TensorFlow

PyTorch

Job description

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

As a Recommendation Algorithm Engineer , you’ll apply state-of-the-art machine learning techniques to build smarter, more personalized match experiences across our social networking products. You'll work on everything from recall to ranking to conversion, integrating cutting-edge models with social features that bring people closer together.

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

• Research and apply techniques such as large-scale deep learning, graph neural networks, multi-objective learning, and reinforcement 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.