Principal Machine Learning Engineer

Responsibilities

• Own the recommendation and search personalization strategy across product surfaces — define the technical approach, prioritize experiments, and drive decisions from offline evaluation to real-world user impact.
• Design and build end-to-end ML systems for ranking, retrieval, and personalization, including data pipelines, feature engineering, model training and evaluation, and production serving infrastructure.
• Drive relevance and ranking improvements through rigorous evaluation and experimentation — design experiments, interpret results, and iterate quickly in response to new content, user behavior, and product surfaces.
• Partner with Product and Engineering to translate model behavior into product outcomes, collaborate on integration, and ensure systems perform reliably in production.
• Collaborate with Data and Security to ensure safe, scalable personalization architecture that addresses data access, privacy constraints, and operational requirements as the platform grows.
• EDUCATION & EXPERIENCE
• Own and drive the recommendation and search personalization strategy across all product surfaces
• Build end-to-end ML systems — data pipelines, feature engineering, model training, and production serving — that scale with the platform
• Drive relevance improvements through rigorous experimentation, ensuring model gains translate to measurable user outcomes
• Build a scalable, privacy-first ML architecture that addresses data access, compliance, and security requirements without slowing delivery

Languages