AIML - Sr. Machine Learning Infrastructure Engineer, Evaluation

Position Overview

How do we ensure that Apple's most advanced AI features perform flawlessly for everyone, everywhere? At Apple, the AI/ML Evaluation team answers this question. We are the architects of quality and trust for AI across all Apple products, from Siri to the iPhone camera. We build the systems and methodologies that rigorously test our models against the complexity and diversity of the real world, ensuring they are robust, fair, and deliver a magical experience.

To truly challenge our models, we must go beyond existing data. This is where you come in. As a Staff Engineer, you will lead the creation of groundbreaking tooling for synthetic data generation. You will architect systems that create vast, diverse datasets — spanning text, images, video, and audio — to simulate the edge cases and future scenarios our models will encounter. Your work will be the foundation for a new paradigm of AI evaluation at Apple.

Key Responsibilities

• Architect a highly scalable, multi-modal platform for generating synthetic data using the latest generative models.
• Design and build robust, high-performance microservices in Golang to serve as the backbone of the data generation platform.
• Operate and scale distributed compute infrastructure, including large Apple internal job scheduling environments and dedicated GPU clusters.
• Develop resilient data pipelines for the curation, processing, and management of massive synthetic datasets.
• Define the technical strategy for integrating synthetic data into our core AI evaluation and testing workflows.
• Collaborate closely with research scientists and ML engineers to develop novel generative models for evaluation purposes.
• Optimize the computational efficiency and scheduling of data generation workloads with a deep focus on maximizing GPU utilization.
• Mentor engineers across the organization on best practices for building and scaling distributed systems for generative AI.

Required Qualifications

• 10+ years of professional software engineering experience building and operating large-scale, high-performance distributed systems.
• Strong programming skills in Go and Python, with proven experience building production services.
• Deep theoretical and practical knowledge of distributed systems principles (e.g., consensus, consistency, scalability).
• Hands-on expertise with container orchestration and infrastructure-as-code in a production environment.
• Experience designing and operating infrastructure for machine learning workloads on GPU compute.
• BS in Computer Science or equivalent work experience.

Preferred Qualifications

• Direct experience architecting systems for training or running large-scale generative models (e.g., Diffusion Models, GANs, LLMs).
• Familiarity with using synthetic data for model testing, validation, robustness checks, or fairness evaluation.
• Architectural ownership of a large-scale ML platform or microservice-based system in a production environment.
• Strategic leadership in defining technical roadmaps and influencing cross-functional teams in an ambiguous, fast-paced domain.
• MS or PhD in Computer Science or a related field.