Machine Learning Test and Automation Engineer, Graphics, Games, and ML - Full-time

**Weekly Hours:** 40 **Role Number:** 200658438-0836 **Summary** The Graphics, Games & Machine Learning (GGML) team is looking for a Machine Learning Test and Automation Engineer to help deliver next-generation Apple Intelligence SW features on both On-Device and Private Cloud Compute. In this role, you will work hand-in-hand with GGML software developers and cross-functional ML teams during all project phases, collaborating on feature definition, quality plan, and test development. We’re looking for someone who is excited to explore new ideas, challenge assumptions, and create innovative approaches to validating complex ML systems. You bring curiosity, creativity, and a passion for quality — and you’re energized by the opportunity to work on technologies that reach millions of users while preserving their privacy. **Description** As an Automation Engineer, you will design and build scalable test solutions that validate both on-device and distributed Apple Intelligence inference. Your work will focus on ensuring the correctness, reliability, and performance of our inference runtime software. You will develop functional and performance tests that push our systems to their limits, helping us deliver fast, efficient, and trustworthy ML-based experiences across Apple’s ecosystem. **Minimum Qualifications** + Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience + 4+ years of proven experience in Software Quality Assurance + Proficiency in Swift or Python + Familiarity with XCTests/Xcode + Experience building scalable automated test frameworks and integrating them into CI/CD pipelines + Creative and analytical problem solver with strong attention to detail; highly organized, self-driven, and motivated to deliver results **Preferred Qualifications** + Familiarity with machine learning frameworks like PyTorch + Understanding of the complete model development lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring