Staff Applied Machine Learning Engineer Intelligent Data Signals and Systems

YOU will design and implement production machine learning systems that turn customer context into trusted signals used by recommendations and decision engines. You will define data and signal contracts specifying intended use freshness provenance confidence eligibility and calibration for downstream consumers. You will own end to end lifecycle of ranking retrieval recommendation search propensity and next best action systems from feature generation to serving experimentation monitoring and feedback loops. You will evaluate impact beyond short term metrics including trust fairness access risk compliance and long term engagement. You will collaborate with product growth data platform modeling risk and compliance to translate ambiguous goals into measurable ML system designs. You will build reusable capabilities and contribute to AI assisted workflows to accelerate development analysis testing and deployment.

Responsibilities

• Build and operate production ML systems that turn customer and product context into trusted signals rankings recommendations and decision capabilities.
• Design production data and signal contracts that define intended use freshness provenance confidence eligibility and calibration for downstream consumers.
• Own ranking retrieval recommendation search propensity and next best action systems end to end from feature generation to serving experimentation monitoring and feedback loops.
• Evaluate impact beyond short term metrics including trust fairness access risk compliance and long term engagement.
• Partner across product growth data platform modeling risk and compliance to translate ambiguous goals into measurable ML system designs.
• Use AI and agents to accelerate development analysis testing documentation and operations while exposing reusable capabilities to product services internal tools and AI assisted workflows.

Requirements

• 12+ years building and operating production software and ML systems for business critical products
• Deep expertise in intelligent systems such as ranking retrieval recommendations search personalization growth and lifecycle ML customer intelligence propensity churn LTV next best action or model derived risk signals
• Strong production ML judgment across feature pipelines model serving experimentation monitoring feedback loops online offline consistency and reliable signal interfaces
• Ability to evaluate impact beyond short term conversion including trust fairness access risk compliance and long term engagement
• Experience using AI assisted engineering tools with appropriate verification testing and review for customer impacting systems

Benefits

• Remote work
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning