Machine Learning Platform Engineer, AI Evaluation Platform (All levels)

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Machine Learning Platform Engineer, AI Evaluation Platform (All levels) Seattle, United States of America Join Apple Services Engineering to build the next generation of AI evaluation systems. We are seeking machine learning platform engineers at multiple levels (Mid-Level to Principal) to architect and build high-availability services and internal tools that enable self-service evaluation at scale. You will partner with researchers to operationalize their innovations, transforming complex workflows into intuitive, developer-first platforms. We are looking for builders who thrive in the ambiguity of new initiatives and are passionate about creating scalable infrastructure. You will join the engineering team responsible for democratizing AI evaluation across the organization. Your focus will be on developing the developer experience-architecting and implementing the APIs, SDKs, and platform services that turn complex evaluation metrics into simple, self-service calls. You will work hand-in-hand with researchers to operationalize sophisticated measurement techniques, ensuring they scale reliably within our high-availability infrastructure. In this role, you will drive the engineering standards for a new organization, upholding the code quality, automation, and testing rigor required to support the rapid evolution of Generative AI and Agentic systems. System Design & Implementation: Design, code, and ship high-quality Python services. For senior candidates: Lead the architecture for the core evaluation engine and distributed services. For mid-level candidates: Own the end-to-end implementation of specific features and API endpoints. Technical Leadership & Collaboration: Mentor junior engineers, conduct code reviews, and drive technical decision-making. Foster a culture of technical excellence and rapid delivery through example and collaboration. Operationalizing Science:

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