Principal AI/ML Engineer

Overview

In this hands-on senior role, you will shape the shared AI/ML and decisioning foundations that power Candy Crush Saga at scale. You will influence architecture, prototype important paths, and ensure reliable, production-grade systems that enable faster experimentation and coherent player experiences. You’ll lead cross-team collaboration to unify fragmented approaches into reusable capabilities. This opportunity combines technical leadership with hands-on coding to drive scalable, player-centric decisions.

Responsibilities

• Define and drive AI/ML and decisioning direction across Candy Crush Saga through credible, clear execution
• Contribute hands-on through production coding, prototyping, and critical-path implementations
• Set high standards for production AI/ML engineering: reliability, observability, maintainability, and quality
• Mentor senior engineers and elevate cross-team design and judgment
• Lead shared AI/ML and automated decisioning architecture used across multiple game components
• Unify fragmented approaches into reusable foundations to improve consistency, speed, and scalability
• Collaborate with product and game teams to convert opportunities into reusable capabilities

Key requirements

• Deep experience shipping production AI/ML systems with tangible business impact
• Strong applied expertise in personalization and causal inference
• Track record of aligning AI/ML methods with production guardrails and operations
• Proven ability to influence technical direction across teams
• Experience defining shared patterns, standards, or architecture in complex environments
• Ability to lead senior-level technical discussions with substance and precision
• Excellent communication to explain complex ideas to technical and non-technical audiences
• Proven ability to align cross-functional teams around a shared technical direction
• Mentoring capability with senior engineers and technical leaders
• Comfort presenting technical work in high-visibility forums

• Cross-functional collaboration
• Strategic influencing
• Mentoring and leadership
• Production AI/ML systems
• Personalization
• Causal inference

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