AIML - Machine Learning Engineer - Computer Vision & Audio, MIND

Apple•Seattle, WA

About The Position

The Machine Intelligence, Neural Design (MIND) team, part of Apple’s AIML organization, is leading Apple-wide innovation on HW/SW co-design for efficient inference. With roots in ML, computer vision, and energy efficiency research, our team is strategically positioned to contribute to diverse initiatives ranging from shipping features in well-known Apple products to ambitious, long-term research projects. We are seeking a hands-on Machine Learning Engineer to drive the data & evaluation lifecycle for our production models. In this role, you will focus on designing and scaling high-performance data processing pipelines, ensuring data quality, performing in-depth failure analysis on production models, and implementing advanced data augmentation techniques to boost model performance. This includes but is not limited to crafting creative techniques to analyze audio & video datasets, designing metrics to understand user behavior & evaluate performance of machine learning models. You will innovate across the entire end-to-end ML production pipeline, bridging the gap between hardware, software, and modeling, ensuring our ML systems are robust, efficient, and scalable. DESCRIPTION We are seeking a Machine Learning Engineer to design and deliver innovative features and models that advance our ML systems. In this role, you will scale model evaluation workflows, build robust data pipelines, and optimize performance across the stack. Your responsibilities will include: Pipeline Scaling & Optimization: Design, build, and maintain scalable ETL/ELT data pipelines using tools like Spark, & Airflow to handle large-scale datasets. Optimize existing pipelines for efficiency, latency, and cost. Data Augmentation & Synthesis: Research and implement advanced data augmentation techniques (e.g., GANs, semantic augmentation, synthetic data generation) to address data scarcity and imbalanced datasets. Data Quality & Monitoring: Implement data observability and automated data validation checks to identify data drift, schema violations, and outliers in real-time. Failure Analysis & Debugging: Perform root-cause analysis on production model failures, diagnosing issues between data inputs and model outputs using advanced statistical methods. Model Evaluation: Collaborate with other machine learning engineers to productize models, implementing robust evaluation frameworks, including experimentation and performance monitoring.

Requirements

Proficiency in working with unstructured data, specifically video & audio signals, for object detection, pattern recognition, feature extraction and segmentation.
Proficiency with Python and deep learning frameworks like PyTorch.
Expertise in designing metrics, and conducting metric change & performance analysis for model evaluation.
Strong problem solving skills in analyzing complex, ambiguous problems and clearly presenting sophisticated technical concepts to both expert and non-expert audiences.
Proven track record of contributing to cross functional projects in a collaborative environment.
Self-motivated and curious with creative and critical thinking capabilities and drive to figure out and improve how things work.
High tolerance for ambiguity. You find a way through. You anticipate. You connect and synthesize.
Strong verbal and written communications skills with demonstrated experience in authoring & presenting analytical insights via papers & presentations.
Experience with large scale training ML models including deep learning based models.
Experience with shipping ML features and products.
Master’s degree or equivalent experience in a technical or quantitative field.

Nice To Haves

Experience with shipping ML features and products
Experience with GPU-based distributed training & evaluation.
Background in Computer Vision (image augmentation), Audio and Natural Language Processing.

Responsibilities

Pipeline Scaling & Optimization: Design, build, and maintain scalable ETL/ELT data pipelines using tools like Spark, & Airflow to handle large-scale datasets. Optimize existing pipelines for efficiency, latency, and cost.
Data Augmentation & Synthesis: Research and implement advanced data augmentation techniques (e.g., GANs, semantic augmentation, synthetic data generation) to address data scarcity and imbalanced datasets.
Data Quality & Monitoring: Implement data observability and automated data validation checks to identify data drift, schema violations, and outliers in real-time.
Failure Analysis & Debugging: Perform root-cause analysis on production model failures, diagnosing issues between data inputs and model outputs using advanced statistical methods.
Model Evaluation: Collaborate with other machine learning engineers to productize models, implementing robust evaluation frameworks, including experimentation and performance monitoring.

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