Niantic’s Machine Learning Team seeks to craft and implement machine learning-powered features for geo-location-based mobile games, responding to player behavior, contextual environments, and geospatial signals.
Requirements
- 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.
- Experience building ML features for mobile games or interactive applications.
- Experience with modern data processing frameworks, e.g. Apache Beam or Apache Spark.
- Exposure to active learning methodologies, recommendation systems and real-time model inference.
- Experience with Cloud native model development environments, e.g. GCP or AWS.
- Familiarity with geo-contextual modeling, map-based data, and temporal-spatial modeling techniques.
Responsibilities
- 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.
Other
- 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.
- 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.
- Required in-office 2 days on Tuesday and Wednesday.
