Senior Manager Machine Learning (Recommendation / Personalization)

At Glance AI, you will be part of an AI commerce platform that is revolutionizing e-commerce by focusing on inspiration-led shopping experiences. Operating globally, Glance AI transforms screens into avenues for instant discovery and joyful shopping. Through cutting-edge AI models integrated with Google's advanced AI platforms, Glance AI offers hyper-realistic shopping experiences across various categories like fashion, beauty, travel, accessories, home dcor, pets, and more. This platform seamlessly integrates into everyday consumer technology, redefining the future of e-commerce with inspiration-driven shopping. Key Responsibilities: - Own personalization and recommendation end to end, including setting the vision, designing core ML systems, and exploring intent-aware, contextual, and generative intelligence. - Lead a team of 810 ML engineers and researchers to develop large-scale, low-latency systems that drive user experience and monetization. - Develop core recommendation and ranking systems, as well as ML-driven ad optimization focusing on relevance, creative selection, pacing, bidding, and long-term user value optimization. - Address user and advertiser modeling, representation learning, explorationexploitation, causal measurement, and online learning while maintaining a balance between engagement, revenue, and user trust. Qualifications Required: - PhD or Masters in a quantitative field like Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research, Economics, Analytics, or Data Science. Alternatively, a Bachelor's degree with relevant experience. - 8 to 10 years of experience in ML / DS teams, applying algorithms like NLP, Reinforcement Learning, Time Series, etc., to solve real-world problems. - Proficiency in software programming and statistical platforms such as Tensorflow, PyTorch, scikit-learn, etc. - Familiarity with distributed training technologies like Apache Spark, RAPIDS, Dask, and MLOps stack such as Kubeflow, MLflow, or their cloud equivalents. - Ability to collaborate effectively with cross-functional teams. - Strong technical and business communication skills to articulate complex ideas to non-technical stakeholders. - High curiosity and quick learning ability to adapt to new subjects rapidly. At Glance AI, you will be part of an AI commerce platform that is revolutionizing e-commerce by focusing on inspiration-led shopping experiences. Operating globally, Glance AI transforms screens into avenues for instant discovery and joyful shopping. Through cutting-edge AI models integrated with Google's advanced AI platforms, Glance AI offers hyper-realistic shopping experiences across various categories like fashion, beauty, travel, accessories, home dcor, pets, and more. This platform seamlessly integrates into everyday consumer technology, redefining the future of e-commerce with inspiration-driven shopping. Key Responsibilities: - Own personalization and recommendation end to end, including setting the vision, designing core ML systems, and exploring intent-aware, contextual, and generative intelligence. - Lead a team of 810 ML engineers and researchers to develop large-scale, low-latency systems that drive user experience and monetization. - Develop core recommendation and ranking systems, as well as ML-driven ad optimization focusing on relevance, creative selection, pacing, bidding, and long-term user value optimization. - Address user and advertiser modeling, representation learning, explorationexploitation, causal measurement, and online learning while maintaining a balance between engagement, revenue, and user trust. Qualifications Required: - PhD or Masters in a quantitative field like Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research, Economics, Analytics, or Data Science. Alternatively, a Bachelor's degree with relevant experience. - 8 to 10 years of experience in ML / DS teams, applying algorithms like NLP, Reinforcement Learning, Time Series, etc., to solve real-world problems. - Proficiency in software programming and statistical platforms such as Tensorflow, PyTorch, scikit-learn, etc. - Familiarity with distributed training technologies like Apache Spark, RAPIDS, Dask, and MLOps stack such as Kubeflow, MLflow, or their cloud equivalents. - Ability to collaborate effectively with cross-functional teams. - Strong technical and business communication skills to articulate complex ideas to non-technical stakeholders. - High curiosity and quick learning ability to adapt to new subjects rapidly.