Machine Learning Scientist, Niantic

• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or related technical field
• 2+ years of experience developing ML systems in a production environment
• Proficiency in Python and ML libraries such as PyTorch, TensorFlow, Scikit-learn, or similar
• Experience with applied ML techniques such as supervised learning, clustering, or deep learning
• Strong analytical and problem-solving skills with a product mentality
• Ability to communicate technical concepts clearly to multi-functional teams
• Ability to work in a fast-paced hybrid environment and handle stress appropriately and/or ability to solve practical problems and be sufficiently adaptable to handle dynamic situations with little advance notice
• Experience working on cross-functional teams with ability to communicate effectively through written and verbal communications, including asynchronous interactions with others

Desirable

• Niantic’s Machine Learning Team seeks a Machine Learning Scientist to join our AI/ML team
• This role focuses on crafting and implementing machine learning-powered features for our geo-location-based mobile games
• You will collaborate closely with product managers, game designers, engineers, and data scientists to craft intelligent game mechanics that respond to player behavior, contextual environments, and geospatial signals
• This is a high-impact, hands-on role ideal for candidates passionate about applying ML to mobile games and real-world data challenges
• Niantic is a leading mobile game developer known for groundbreaking real-world mobile gaming experiences
• Our mission is to use new technologies to enrich the human experience and encourage exploration of the world around us
• Titles like Pokémon GO, Pikmin Bloom, and Monster Hunter Now are just the beginning
• We’re looking for curious, creative, and purposeful minds to join us on our journey
• Craft and develop machine learning models that drive intelligent gameplay features, such as multifaceted content placement, player clustering, and geo-contextual personalization
• Leverage real-world data (geospatial, temporal, behavioral) to advise in-game decision-making and adapt to player environments in real time
• Partner with product and design teams to translate gameplay ideas into ML-powered systems
• Collaborate with data science and data engineering teams to optimize data pipelines for machine learning use cases
• Collaborate with product and data science teams to perform thorough experimentation, A/B testing, and model evaluation to measure product impact and feature efficiency
• Contribute to the design of ML infrastructure, tools, and workflows that support the lifecycle of models in production