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通用信息

地点:Vancouver, British Columbia, Canada 
  • 地点: Vancouver
  • 州:
  • 国家/地区: Canada

  • 地点: Toronto
  • 州:
  • 国家/地区: Canada


角色 ID
204265
工作人员类型
Regular Employee
工作室/部门
CTO - Studio Data Insights
弹性工作安排
Hybrid

描述和要求

我们是一支全球化团队,由创作者、剧作家、技术专家、体验创作者和创新者等角色组成。我们相信,精彩的游戏与体验始于与玩家和社区同样多样化的团队。在 Electronic Arts,你的想象力是对你的唯一限制。

The Data & Analytics (DnA) function brings both quantitative and qualitative insights to the entire company (including all game teams) through Studio Analytics, Data Engineering, Data Science and Machine Learning Engineering. We develop our Studio's data technical capabilities to bring data insights right to the studio partners' fingertips.

We are looking for a Machine Learning engineer who will work amidst unique technologies to deliver in-game Personalization by building global-scale production systems. You will report to the technical director of AI/ML. If you have experience creating advanced analytics data products to allow partners to make crucial decisions, you want to make impacts to help player engagement by providing an autonomous platform; then we want to talk to you. This role allows for flexible work including remote and hybrid options

Responsibilities:

  • Lead the design and implementation of scalable architecture for ML systems.
  • Establish and enforce best practices in MLOps and ML model production across projects.
  • Collaborate with cross-functional teams to improve and deploy production-ready ML models.
  • Promote the use of the latest technologies within the team.
  • Provide strategic guidance and mentorship to team members to ensure efficient deployment and scalability of ML solutions.
  • Maintain knowledge of industry trends and integrate relevant advancements into project architectures.

Requirements:

  • Bachelor's degree in Computer Science, Data Science, or a related field.
  • 6+ years of industry experience in solution architecture, with a focus on Machine Learning and MLOps.
  • Experienced in designing and maintaining complex ML systems.
  • Knowledge of MLOps practices and ML model production.
  • Experience with cloud platforms and tools such as AWS, GCP, Airflow, Kubernetes, Docker, and CI/CD practices
  • Experience in mentorship.
  • Experiencework in a cross-functional team environment.
  • Provide technical leadership in adopting new technologies.