AI/ML Vision & Audio Engineer — Scalable ML Pipelines

Apple Inc.; Seattle (WA); On-site; USD 142,300 - 263,300; Full time

Job summary

Apple Inc. in Seattle, Washington, is seeking a hands-on Machine Learning Engineer to design and scale data processing pipelines for production models. You will bridge hardware, software, and modeling to ensure robust, efficient, and scalable ML systems.

You will design scalable ETL/ELT data pipelines (Spark, Airflow), develop data augmentation techniques, monitor data quality, perform failure analysis, and collaborate on model evaluation and deployment across the stack.

Qualifications

• Proficiency with unstructured video/audio signals for detection, recognition, feature extraction and segmentation.
• Proficiency with Python and PyTorch.
• Expertise in designing metrics and analyzing metric changes for model evaluation.
• Strong problem‑solving skills and ability to convey complex ideas clearly to various audiences.
• Master’s degree or equivalent in a technical or quantitative field.

Responsibilities

• Pipeline Scaling & Optimization: build scalable ETL/ELT pipelines using Spark and Airflow for large-scale data.
• Data Augmentation & Synthesis: implement advanced augmentation and synthetic data techniques.
• Data Quality & Monitoring: implement data observability, validation checks, drift and outlier detection.
• Failure Analysis & Debugging: perform root-cause analysis of production model failures.
• Model Evaluation: collaborate to productize models and implement robust evaluation frameworks.

Skills

Video/Audio signals

Python

PyTorch

Metric design

Problem solving

Master's degree in technical field

Education

Master's degree or equivalent in technical field

Tools

Spark

Airflow

Job description

Apple Inc. in Seattle, Washington, is seeking a hands-on Machine Learning Engineer to design and scale data processing pipelines for production models. You will bridge hardware, software, and modeling to ensure robust, efficient, and scalable ML systems.

You will design scalable ETL/ELT data pipelines (Spark, Airflow), develop data augmentation techniques, monitor data quality, perform failure analysis, and collaborate on model evaluation and deployment across the stack.