Apple is seeking candidate for the role of Machine Learning Engineer in Data Quality to ensure integrity, accuracy, and reliability.
Position overview
| Job Title | Machine Learning Engineer |
| Team | Data Quality |
| Experience | 3+ years |
| Skills | Python, SQL and databases |
| Location | Bangalore |
About the Role
The Machine Learning Engineer will design and build scalable ML-driven solutions for data validation, anomaly detection, and quality monitoring—helping Apple’s global operations run with precision and confidence. If you’re passionate about applying cutting-edge machine learning to solve real-world data challenges at scale, come join us in pushing the boundaries of what’s possible.
Responsibilities
- 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.
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- 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
Minimum Qualifications
- 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.
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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.
Location
Bangalore
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