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Machine Learning Engineer

The Analytics Solutions group within the EA Studios technology organization, which is the part of the group that has a focus on engineering to support analytics and data science to improve player experience. The problems we are solving are around games. From store recommendation on racing titles to matchmaking on shooter genre, we aim to improve player experience through machine learning. We are looking for a senior-level engineer to work on machine learning products as solutions that can transform and apply complex statistical models and data pipelines to empower better experience in games' live service. You will report to the technical director of ML and applications. If you have passion for building advanced analytics data products to allow partners to make important decisions; if you want to make impacts to help player engagement by providing an autonomous platform; then we want to talk to you.

Responsibilities

• Use statistical and machine learning techniques to create scalable and end-to-end data science systems.
• Analyze large amounts of players data with business use cases to bridge the gaps of data-driven cycles.
• Write production-quality code that solves real-world problems, in any of our supported algorithm development languages and frameworks.
• Work with software engineering teams such as architecture, data engineering, and infrastructure to improve real-time model implementations and new feature creations.
• Establish scalable, efficient, automated processes for large-scale data analyses, model development, model validation and model implementation.
• Track post-ML deployment general business activity and provide compelling management reporting on a regular basis.
• Research and implement novel machine learning and statistical approaches.
• Pilot to adopt artificial intelligence onto the business.

Qualifications

• B.S. in Data Science, Machine Learning, Statistics, Computer Science, Applied Math, or an equivalent technical field.
• 4+ years of professional software development experience in backend development experience with an interest in work involving data pipelines, distributed systems, performance analysis, and large-scale data processing.
• 3+ years of applied ML experience in statistical and mathematical modeling such as supervised and unsupervised machine learning, deep learning, or reinforcement learning.
• Familiarity with one or more deep learning software frameworks such as Tensorflow, Caffe and PyTorch.
• Strong programming skills in Scala, Java, Python, or similar language.
• Experience building, deploying and monitoring large-scale ML models in production.
• Experience with one or more container ecosystems such as Docker and Kubernetes.
• Experience with AWS (S3, sagemaker, greengrass, monitoring with new relic), GCP (ComputeEngine, CloudGPUs, cloud storage, pub/sub), or similar
• Experience in CI/CD pipeline tools such as Gitlab and Jenkins.
• Experience working in an agile team environment.

Bonus

• Ph.D. in Data Science, Machine Learning, Statistics, Computer Science, Applied Math, or an equivalent technical field
• Explored modern data storage, messaging, and processing tools (Kafka, Spark, Hadoop, Cassandra, etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar.
• Experience in ML/AI product development lifecycle such as model versioning, feature store, hyper-parameter tuning, and logs/metrics visualizations and monitoring.
• Familiar with concepts related to testing and maintaining models in production such as A/B testing, retraining, monitoring model performance.
• Experience in several of the following areas: machine learning, statistics, deep learning, NLP, recommendation systems, dialogue systems, information retrieval.