Machine Learning Software Engineer

At Apple, we rely on high-quality data to drive critical decisions across our global operations. We are looking for a Machine Learning Engineer with software engineering skills to develop and deploy ML-driven data validation solutions that ensure data integrity. In this role, you will build scalable anomaly detection systems, work on Gen AI projects, collaborate with data engineering teams to enhance data quality frameworks, and drive innovations in MLOps and data monitoring.- Develop ML-based data validation and monitoring solutions, focusing on anomaly detection and explainability.- Analyze large datasets to detect data drift, integrity issues, and emerging quality risks.- Apply the full ML lifecycle, from exploratory data analysis (EDA) and feature engineering to model selection, training, deployment, and monitoring.- Experiment with different methodologies to improve model accuracy and reliability.- Collaborate with data engineering teams to design frameworks for detecting missing, inconsistent, or duplicate data.- Investigate root causes of data quality issues and propose scalable, automated solutions.- Stay up to date with the latest advancements in data science, MLOps, and data engineering best practices. 3+ industry experience in building ML solutions and collaborating with software teams. Strong experience in machine learning for anomaly detection, data validation, or data quality improvement. Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, etc.). Hands-on experience with SQL and databases (PostgreSQL, Snowflake, MySQL, etc.). Strong knowledge of statistical methods (PCA, exponential smoothing, and etc.) for detecting anomalies, drift, and inconsistencies. Experience with version control (Git) and software development best practices. Preferred Qualifications Experience with MLOps tools (MLflow, Kubeflow) for managing data quality models. Exposure to big data frameworks (Spark, Kafka) for real-time data validation. Familiarity with CI/CD for data pipelines and model deployments. Strong problem-solving skills and ability to diagnose complex data issues. Experience working with large-scale structured and unstructured data. Familiarity with data engineering concepts, including ETL pipelines, batch/stream processing.