Principal ML Engineer

Own the technical direction for Copilot Discover platforms, setting end-to-end architectural strategy. Partner with product, design, data science, and engineering leaders to translate business and user needs into executable architectural plans, well-documented designs, and multi-year roadmaps Set and govern architectural decisions across multiple services and teams, ensuring systems are scalable, secure, reliable, cost-efficient, and grounded in data, telemetry and operational excellence. Raise the technical bar across the organization by establishing falsifiable principles, reviewing critical designs, and helping to develop technical leaders within the team. Establish and evolve quality and reliability standards, including test strategies, CI/CD practices, monitoring, alerting, and live-site health. Shape the adoption of AI/ML techniques for content understanding, personalization, summarization, and safety, in close collaboration with MAI and partner teams. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research) Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Proven experience designing and operating large-scale distributed systems, including data pipelines, microservices, APIs, and storage systems.

with content platforms, personalization systems, or consumer-facing services at scale.

with technologies such as Apache Spark, Kafka, columnar storage, data modeling, and schema evolution.

Demonstrated success as a technical lead or architect, influencing across teams without direct authority.

Proven experience and understanding of system architecture, performance tuning, telemetry design, and operational excellence.

Exposure to ML/AI systems, especially in content understanding, ranking, or personalization.

Excellent analytical and communication skills, with the ability to clearly articulate complex technical concepts.

Demonstrated cross-organizational collaboration skills and the ability to influence senior stakeholders.