Software Development Engineer

Apple•Cupertino, CA

1d•$181,100 - $272,100•Onsite

About The Position

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn’t have imagined, and now, can’t imagine living without. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do. Design, automate, and optimize AI deployment pipelines for large-scale model serving using python and internal tools. Implement end-to-end data quality monitoring, validation, and anomaly detection for AI systems using python and internal tools. Develop comprehensive testing, benchmarking, and continuous evaluation frameworks for AI models using python and internal tools. Collaborate with machine learning engineers and infrastructure teams to integrate and optimize AI models in production using python and internal tools. Generate and automate data labeling pipelines using LLMs for large-scale AI datasets including python and internal tools. 40 hours/week.

Requirements

Bachelor's Degree or foreign equivalent in Cognitive Science, Computer Science or Machine Learning or related field
4 years of experience in the job offered or related occupation.
4 years of experience with Implementing and develop metrics and add them to the monitoring pipelines to be used for quality measurement and monitoring
4 years of experience with Utilizing designing, automating, and optimizing data ingestion, transformation experience, validation, and quality assurance workflows at scale to manage training and evaluation data.
4 years of experience with Integrating AI models into production, and monitoring performance for latency, drift, and reliability.
4 years of experience with Setting up CI/CD pipelines for deploying AI-driven applications, automating data and model versioning, and managing reproducibility.
4 years of experience with Utilizing semi-supervised learning, human-in-the-loop data labeling, and synthetic data generation using LLMs to scale annotation efforts.
4 years of experience with Developing and maintaining software tooling in Python for data processing, management, and automation
4 years of experience with Communicating risks and insights related to model quality and data pipeline performance to technical and non-technical stakeholders, including leadership teams.
4 years of experience with Applying strong software engineering principles, including code testability, automation, metric-driven development, and adaptability to new technologies through documentation code reviews, design patterns, and proven architectural frameworks.

Responsibilities

Design, automate, and optimize AI deployment pipelines for large-scale model serving using python and internal tools.
Implement end-to-end data quality monitoring, validation, and anomaly detection for AI systems using python and internal tools.
Develop comprehensive testing, benchmarking, and continuous evaluation frameworks for AI models using python and internal tools.
Collaborate with machine learning engineers and infrastructure teams to integrate and optimize AI models in production using python and internal tools.
Generate and automate data labeling pipelines using LLMs for large-scale AI datasets including python and internal tools.
Implement and develop metrics and add them to the monitoring pipelines to be used for quality measurement and monitoring.
Utilize designing, automating, and optimizing data ingestion, transformation experience, validation, and quality assurance workflows at scale to manage training and evaluation data.
Integrate AI models into production, and monitor performance for latency, drift, and reliability.
Set up CI/CD pipelines for deploying AI-driven applications, automating data and model versioning, and managing reproducibility.
Utilize semi-supervised learning, human-in-the-loop data labeling, and synthetic data generation using LLMs to scale annotation efforts.
Develop and maintain software tooling in Python for data processing, management, and automation.
Communicate risks and insights related to model quality and data pipeline performance to technical and non-technical stakeholders, including leadership teams.
Apply strong software engineering principles, including code testability, automation, metric-driven development, and adaptability to new technologies through documentation code reviews, design patterns, and proven architectural frameworks.