Machine Learning

Apple•San Diego, CA

9h•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 and apply advanced machine learning research to address data-centric problems. Develop frameworks for data selection, curation, and annotation for multimodal foundation models. Innovate and implement machine learning algorithms to create high-quality, impactful datasets. Explore and research methods to optimize training workflows for foundation modes at scale. Build and maintain evaluation pipelines to measure and benchmark the performance of foundation models. Apply computer vision techniques to perception and scene understanding tasks. Develop and evaluate models for visual recognition, object detection, semantic segmentation, and spatial reasoning. Integrate visual data into multimodal learning systems for enhanced scene comprehension and interpretation. Implement and optimize large-scale distributed training workflows for machine learning models with billions of parameters. Develop and maintain training pipelines using PyTorch for scalable and efficient model development.

Requirements

Master’s Degree or foreign equivalent in Electrical Engineering, Machine Learning, AI or related field and 6 months of experience in the job offered or related occupation.
Training state-of-the-art machine learning models for classification, object detection, semantic segmentation, video understanding and other perception tasks.
Leveraging synthetic data to train machine learning models and using advanced domain adaptation techniques to help models generalize to real world scenarios.
Training large AI Models with Billions of Parameters.
Creating and optimizing large datasets with tens of millions of images to train Foundation Models
Developing machine learning driven pipelines to automatically annotate data, including describing the images, segment objects in the images.
Developing novel algorithms to estimate and quantify data selection and data impact on AI models
Training computer vision and language models with self-supervised learning algorithms to reconstruct 3D representations of scenes using images
Building and training large machine learning models using distributed training frameworks based on Python, PyTorch and Tensorflow.

Responsibilities

Design and apply advanced machine learning research to address data-centric problems.
Develop frameworks for data selection, curation, and annotation for multimodal foundation models.
Innovate and implement machine learning algorithms to create high-quality, impactful datasets.
Explore and research methods to optimize training workflows for foundation modes at scale.
Build and maintain evaluation pipelines to measure and benchmark the performance of foundation models.
Apply computer vision techniques to perception and scene understanding tasks.
Develop and evaluate models for visual recognition, object detection, semantic segmentation, and spatial reasoning.
Integrate visual data into multimodal learning systems for enhanced scene comprehension and interpretation.
Implement and optimize large-scale distributed training workflows for machine learning models with billions of parameters.
Develop and maintain training pipelines using PyTorch for scalable and efficient model development.