About the Company

At Humai, we're pioneering the future of AI-powered products that seamlessly integrate into people's daily lives. We believe in building intelligent systems that not only push the boundaries of what's technically possible but also create meaningful value for our users. Our team combines deep technical expertise with a product-first mindset, focusing on shipping features that users love while maintaining the highest standards of AI safety and reliability. We're a fast-moving, engineering-driven company that values innovation, ownership, and impact. Our culture emphasizes rapid iteration, data-driven decision making, and collaborative problem-solving.

About the Role

You'll lead the development of advanced recommendation and matching systems that analyze complex user profiles and behaviors to create meaningful connections. This role requires someone who can translate nuanced human preferences into scalable algorithms while maintaining fairness and user privacy.

Responsibilities

• Design and implement state-of-the-art recommendation algorithms for reciprocal matching scenarios
• Build ML pipelines that process diverse data types including text, images, and behavioral signals
• Develop real-time inference systems capable of handling millions of user interactions
• Create sophisticated feature engineering pipelines that capture subtle compatibility signals
• Implement fairness constraints and bias mitigation strategies in production systems
• Design and run A/B tests to continuously improve matching quality
• Build feedback loops that learn from user interactions and outcomes
• Collaborate with product teams to translate user needs into technical solutions

Qualifications

Required Qualifications

Technical Expertise:

• 5+ years of experience building production recommendation systems
• Strong background in collaborative filtering, content-based filtering, and hybrid approaches
• Expertise in deep learning frameworks (PyTorch/TensorFlow) and modern NLP techniques
• Experience with approximate nearest neighbor search and high-performance computing
• Proven track record deploying and maintaining ML models at scale
• Proficiency in Python and SQL, with experience in distributed computing (Spark/Dask)

Domain Knowledge:

• Experience with two-sided marketplace matching problems
• Understanding of ranking algorithms and multi-objective optimization
• Knowledge of graph algorithms and network analysis
• Familiarity with contextual bandits and exploration/exploitation trade-offs
• Experience implementing privacy-preserving ML techniques

Production Skills:

• Strong MLOps experience with model versioning, monitoring, and deployment
• Experience building real-time inference systems with sub-second latency requirements
• Track record of optimizing systems for millions of users
• Experience with A/B testing frameworks and statistical analysis

Preferred Qualifications

• Advanced degree in Computer Science, Machine Learning, or related field
• Experience at consumer tech companies with large-scale recommendation systems
• Publications or contributions to open-source ML projects
• Knowledge of fairness in ML and algorithmic bias research
• Experience with computer vision for image-based features
• Understanding of behavioral psychology or social network dynamics

What You'll Work On

• Building algorithms that understand compatibility across thousands of dimensions
• Creating systems that balance multiple objectives: user satisfaction, engagement, and long-term success
• Developing novel approaches to the cold start problem
• Implementing cutting-edge research in production environments
• Solving unique challenges in reciprocal recommendation systems