ML Engineer

Role Overview: As the founding ML lead at the company, you will be responsible for designing and deploying the core recommendation and personalization systems for a next-generation matchmaking platform. Your role will involve full ownership of the lifecycle, from ideation to production, of scalable, real-time ranking infrastructure that transforms sparse and dynamic user behavior into deeply personalized match suggestions. Key Responsibilities: - Build and scale matchmaking, recommendation, ranking, and personalization systems from scratch - Design a real-time adaptive matchmaking engine that evolves with user interactions and multi-modal signals - Develop algorithms for curated, personalized user feeds using ranking models and recommender systems - Construct user embeddings, similarity frameworks, and graph-based scoring models - Solve cold-start challenges with novel approaches including meta-learning and LLM-based signals - Collaborate closely with product, data, and engineering teams to ensure cohesive user experiences - Deploy ML models into production with strong observability, experimentation, and model management practices - Help establish and grow a high-performance ML team and culture Qualifications: - 3-10 years of hands-on experience in building large-scale personalization, recommendation, or ranking systems - Strong track record with B2C platforms, especially social, e-commerce, dating, gaming, fashion, or video - Deep expertise in collaborative filtering, deep retrieval, learning-to-rank, embedding models with ANN, and LLM-based recommendation for sparse data - Practical experience with building end-to-end ML pipelines from feature engineering to deployment - Clear understanding of offline/online evaluation, metric optimization, and A/B testing at scale - Exposure to vector search, graph-based modeling, and reinforcement learning is a strong plus (Note: Omitting additional company details as it was not included in the job description) Role Overview: As the founding ML lead at the company, you will be responsible for designing and deploying the core recommendation and personalization systems for a next-generation matchmaking platform. Your role will involve full ownership of the lifecycle, from ideation to production, of scalable, real-time ranking infrastructure that transforms sparse and dynamic user behavior into deeply personalized match suggestions. Key Responsibilities: - Build and scale matchmaking, recommendation, ranking, and personalization systems from scratch - Design a real-time adaptive matchmaking engine that evolves with user interactions and multi-modal signals - Develop algorithms for curated, personalized user feeds using ranking models and recommender systems - Construct user embeddings, similarity frameworks, and graph-based scoring models - Solve cold-start challenges with novel approaches including meta-learning and LLM-based signals - Collaborate closely with product, data, and engineering teams to ensure cohesive user experiences - Deploy ML models into production with strong observability, experimentation, and model management practices - Help establish and grow a high-performance ML team and culture Qualifications: - 3-10 years of hands-on experience in building large-scale personalization, recommendation, or ranking systems - Strong track record with B2C platforms, especially social, e-commerce, dating, gaming, fashion, or video - Deep expertise in collaborative filtering, deep retrieval, learning-to-rank, embedding models with ANN, and LLM-based recommendation for sparse data - Practical experience with building end-to-end ML pipelines from feature engineering to deployment - Clear understanding of offline/online evaluation, metric optimization, and A/B testing at scale - Exposure to vector search, graph-based modeling, and reinforcement learning is a strong plus (Note: Omitting additional company details as it was not included in the job description)