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Apple • Sunnyvale, California, United States
Role & seniority: Algorithm/ML Validation Engineer (experienced; technical leadership role)
Stack/tools: Python; ML frameworks (PyTorch, TensorFlow, or JAX); CV/ML evaluation pipelines; data visualization tools (Tableau or Superset)
Design and implement evaluation pipelines for Apple’s ML systems; lead validation efforts
Partner with algorithm teams to design live test procedures, aggressor searches, user studies, and annotation pipelines
Design experiments to expose vulnerabilities, assess end-to-end system performance, and relate findings to customer experience
Bachelor’s degree in CS, ML, EE, Statistics, or related field; 3+ years in ML/AI system development or validation
Strong Python skills; experience testing products with computer vision, computational photography, generative AI, or related areas
Effective communication and collaboration across partner teams; commitment to inclusive environments
Master’s/PhD in ML, CV, Statistics or related field; 5+ years in ML; debugging/deploying models
Expertise in statistical experimental design and hypothesis testing
Hands-on with PyTorch, TensorFlow, or JAX; data analysis/visualization (Tableau, Superset)
Understanding of testing sensing technologies and evaluating user impact/performance
Location & work type: Location not specified; work type not specified.
We are the Product Systems Quality team, and we are looking for a highly motivated and experienced Algorithm Validation Engineer with a passion for delivering robust, inclusive, and state-of-the-art Computer Vision and Machine Learning algorithms in Apple’s next generation of products. You'll enjoy working on a team of quality engineers with diverse backgrounds as we refine the model pipelines that power Apple’s trademark simple and elegant user experience. Come be a part of our team and use both creativity and technical expertise to bring experiences to life that our customers love!
DESCRIPTION
We are seeking an experienced Machine Learning Validation Engineer to lead the design and implementation of evaluation pipelines for Apple’s ML systems. This is a technical leadership role for someone who bridges algorithm research, systems engineering, and customer experience. You’ll be solving complex problems at the intersection of model performance, real-world constraints, and user impact. Your work will require close partnership with algorithm development teams to design and execute live test procedures, aggressor searches, user studies, and annotation pipelines to improve and influence algorithm performance and design. You’ll use data science techniques to design experiments that expose vulnerabilities in the models and investigate patterns of failure. By focusing on end-to-end system performance, you'll evaluate and represent the true customer experience while using a deep understanding of the various components within the models to test comprehensively and efficiently. You’ll also work with hardware and software engineering teams to consider the system design and external factors that influence model performance. You'll be working through every step of the product development cycle and will help make Apple products more reliable, flexible, and easy to use.
MINIMUM QUALIFICATIONS
Bachelor's degree or equivalent in Computer Science, Machine Learning, Electrical Engineering, Statistics, or related field A minimum of 3 years of hands-on industry experience developing or validating ML/AI systems Strong programming skills and hands-on experience with Python. Experience in testing products utilizing computer vision, computational photography, generative AI, machine learning, or related areas. Ability to communicate effectively and collaborate with partner teams. Committed to encouraging an open and inclusive work environment.
PREFERRED QUALIFICATIONS
Master's or PhD in Machine Learning, Computer Vision, Statistics, or related field 5 or more years of ML industry experience, including time spent debugging or improving deployed models Strong background in statistical experimental design and hypothesis testing Hands-on experience with PyTorch, TensorFlow, or JAX—including model analysis and interpretability tools Data analysis, visualization, and reporting experience with tools such as Tableau or Superset Understanding of how to test and quantify performance of sensing technologies such as camera, IMU, capacitive, environmental, light, motion, radar, optical, acoustic, and evaluate user impact and performance.