Machine Learning Engineer

Apple•Cupertino, CA

9d•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. APPLE INC has the following available in Cupertino, California and various unanticipated locations throughout the USA.

Requirements

Master’s degree or foreign equivalent in Computer Science, Computer Engineering or related field and two years of experience in the job offered or related occupation.
Utilizing Python, Java, SQL DB, and Dagster to design and implement data curation and data dashboard software
Utilizing Python, Pytorch, Docker, and cloud compute to design and implement machine learning (ML) powered data filtering and analysis pipeline
Utilizing the knowledge of ML and computer vision to develop ML models and training solutions
Utilizing ML knowledge, Python, and Pytorch to implement ML training and evaluation software and infrastructure
Designing and implementing computer vision data curation algorithms, including representation learning, confident learning, out of distribution detection, and 3D reconstruction.
Experience in vision data processing tools including scikit-image and OpenCV.
Experience in data pipeline, modeling, database and query tools, including Dagster and PostgreSQL.

Responsibilities

Collaborate with cross functional teams to develop computer vision data generation solutions.
Design and implement computer vision and deep learning-based algorithms to generate data ground truth.
Design and implement front-end software for web-based data management applications.
Design and implement back-end software for cloud-based data processing.
Implement and maintain software pipeline to analyze computer vision data integrity.
Develop online data dashboard for data tracking and analytics.
Develop machine learning models to analyze the distribution of computer vision data.
Develop data annotation tools with integrated machine learning models for efficient data annotation.