Data Scientist - Hardware Acoustics

Cupertino, California, United States Hardware

The Hardware Acoustic organization at Apple is dedicated to delivering industry-leading audio experiences across all our products. We are a multidisciplinary team of acoustic engineers, researchers, and machine learning experts who push the boundaries of sound quality, noise cancellation, and user interaction.

We are a Machine Learning Data team and are responsible for building the robust data infrastructure, pipelines, and analytical tools that enable the development, training, and evaluation of cutting-edge machine learning models for acoustic applications. We work with vast, complex datasets, ensuring their quality, accessibility, and utility for our ML scientists and engineers.

We are seeking a highly motivated and skilled Data Scientist/Engineer to join our Machine Learning Data team within Hardware Acoustics. This role sits at the intersection of data engineering, data science, and machine learning, with a specific focus on acoustic and sensor data. You will be instrumental in designing, developing, and maintaining scalable data pipelines, ensuring data quality, and preparing complex datasets that power machine learning models enhancing Apple's hardware acoustic performance.

You will collaborate closely with ML engineers, acoustic scientists, and hardware engineers to understand their data needs and deliver impactful, data-driven solutions.

• Design, develop, and maintain robust and scalable data pipelines for collecting, processing, and transforming large volumes of acoustic, sensor, and related metadata.
• Collaborate with acoustic engineers and ML scientists to identify, extract, and engineer features from raw acoustic data for machine learning models.
• Implement rigorous data quality checks, monitoring, and anomaly detection to ensure the integrity, reliability, and privacy of data used for ML.
• Develop tools and frameworks to automate data ingestion, validation, preparation, and labeling processes.
• Perform exploratory data analysis (EDA) and data visualization to uncover insights, identify trends, and communicate findings to cross-functional teams.
• Contribute to the definition of data schemas, data governance, and best practices for data management within the Hardware Acoustic organization.
• Support the deployment and monitoring of ML models by ensuring data consistency between training and inference environments.
• Optimize data storage, access patterns, and query performance for large-scale datasets.
• Thoroughly document data pipelines, schemas, processing logic, and data dictionaries.

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, or a related quantitative field.
• 3+ years of professional experience in data engineering, data science, or machine learning engineering roles, with a strong focus on data pipelines and data preparation.
• Expert proficiency in Python for data manipulation, scripting, and automation.
• Strong SQL skills for complex data querying, analysis, and database management.
• Experience with distributed data processing frameworks (e.g., Apache Spark, Hadoop).
• Solid understanding of data warehousing concepts, data modeling, and ETL/ELT principles.
• Experience with version control systems (e.g., Git).

• Experience working with acoustic data, audio signal processing, or sensor data.
• Familiarity with machine learning concepts and experience using ML libraries (e.g., scikit-learn, Tensor Flow, PyTorch).
• Experience with MLOps principles and practices for managing the ML lifecycle.
• Knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).
• Experience with real-time data processing or streaming technologies.
• Excellent problem-solving, analytical, and communication skills, with the ability to explain complex data concepts to diverse audiences.
• Familiarity with web front/back end.
• Familiarity with large-scale data platforms and services (e.g., cloud-based data warehouses/lakes or similar internal infrastructure).

At Apple, base pay is one part of our total compensation package and is determined within a range. The base pay range…