ML Test & Automation Engineer
Apple
Software Engineering, Data Science
Sunnyvale, CA, USA
USD 141,800-258,600 / year + Equity
Posted on Dec 10, 2025
The Video Engineering organization is at the forefront of developing innovative technologies for future Apple products, with a strong focus on biometric authentication. Our team has contributed to impactful projects such as FaceID for iOS and OpticID for Apple Vision Pro. We are currently looking for a talented ML Test & Automation Engineer to ensure the quality, reliability, and performance of Apple's machine learning based biometric systems that secure and enhance the lives of millions of users around the world.
Video Engineering DAQ (Data Analytics and Quality) team is seeking a highly motivated and technically skilled ML Test & Automation Engineer to ensure the quality and reliability of FaceID through both manual on-device testing and automated testing infrastructure. You will design end-to-end testing flows that cover model accuracy, edge case detection, and regression testing on actual devices, while performing manual on-device UX testing to assess authentication flows and failure scenarios. You will build scalable automation pipelines to reduce manual testing efforts and enable continuous validation across Apple's device portfolio. By collaborating closely with cross functional teams including deep learning, software engineering, and hardware integration, you will understand model architectures and failure modes to design targeted tests that catch issues early in the development cycle. Your insights will directly guide FaceID development, ensuring the highest standards of security and user experience. A strong background in both software engineering and machine learning is essential to bridge the gap between model development and production deployment.
- BS and a minimum of 3 years relevant industry experience
- 3+ years of testing experience
- Strong programming skills in Python with emphasis on test framework development
- Solid understanding of machine learning concepts, model deployment, and on-device inference
- Proven experience building automated testing infrastructure and CI/CD pipelines
- Demonstrated ability to design comprehensive test plans covering functional, performance, and stress testing scenarios
- Familiarity with version control systems (Git) and collaborative development workflows
- Strong analytical and debugging skills with attention to detail in identifying edge cases and failure modes
- Experience testing computer vision or biometric authentication systems
- Knowledge of iOS development
- Experience with agentic testing workflows and AI-driven test generation
- Familiarity with ML model optimization techniques for on-device deployment (quantization)
- Experience with device farm management
- Experience with cloud computing platforms (AWS) for test automation and data processing
- Experience with test data generation and synthetic data creation for ML systems
- Excellent communication skills and ability to work effectively across multiple teams
- Self-motivated with strong problem-solving abilities and passion for quality engineering
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