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일반 정보

지역: Vancouver, British Columbia, Canada 
  • 장소: Vancouver
  • 시/도:
  • 국가: Canada

  • 장소: Toronto
  • 시/도:
  • 국가: Canada


역할 ID
204265
근로자 유형
Regular Employee
스튜디오/부서
CTO - Studio Data Insights
유연근무제
Hybrid

설명 및 참여 요건

당사는 크리에이터, 스토리텔러, 기술자, 경험 생산자, 혁신가 등으로 구성된 글로벌 팀입니다. 당사는 서비스를 제공하는 플레이어만큼이나 다양한 팀에서 놀라운 게임과 경험이 시작된다고 믿습니다. Electronic Arts에서 불가능은 없습니다.

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.