Machine Learning Engineer, AiDP at Apple

, California, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 26

Salary

0.0

Posted On

03 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Structures, Algorithms, Feature Engineering, Model Training, Model Serving, Model Monitoring, Model Refresh Management, NLP, GenAI, Cloud Platforms, Containerization, Data Processing, Model Evaluation, Hyper-Parameter Tuning, Model Compression, Optimization Techniques

Industry

Computers and Electronics Manufacturing

Description

The people here at Apple don’t just build products — we craft the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that supports the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. A passion for product ownership and track record will prove critical to success on our team. Be ready to make something extraordinary when here. Multifaceted, encouraging people and innovative, industry-defining technologies are the norm at Apple. Would you like to work in a fast-paced environment where your technical abilities will be challenged on a day to day basis? Join the AI & Data Platform (AiDP) team and empower Apple's business groups with cutting-edge ML, data, and analytics solutions. This position is an extraordinary opportunity for a competent, experienced, and results-oriented machine learning engineer to define and build some of the best-in-class machine learning platforms and products that drive business outcomes across Retail, iTunes, Marketing, and more. DESCRIPTION Our Machine Learning Engineers work on building intelligent systems to democratize AI across a wide range of solutions within Apple. You will drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Apple’s products and services. You will implement robust, scalable ML infrastructure, including data storage, processing, and model serving components, to support seamless integration of AI/ML models into production environments. You will develop novel feature engineering, data augmentation, prompt engineering and fine-tuning frameworks that achieve optimal performance on specific tasks and domains. You will design and implement automated ML pipelines for data preprocessing, feature engineering, model training, hyper-parameter tuning, and model evaluation, enabling rapid experimentation and iteration. You will also implement advanced model compression and optimization techniques to reduce the resource footprint of language models while preserving their performance. There are massive opportunities for you deliver impactful influences to Apple. MINIMUM QUALIFICATIONS Strong proficiency in programming languages like Java or Python Solid understanding of Data Structures and Algorithms. 4+ years of machine learning engineering experience in feature engineering, model training, model serving, model monitoring, and model refresh management. 2+ years experience working with NLP and GenAI frameworks (LangChain, LlamaIndex, etc.) Experience with cloud platforms (AWS, GCP) and containerization technologies (Docker, Kubernetes). PREFERRED QUALIFICATIONS Experience handling large-scale datasets and data-driven applications. Familiarity with embedding, retrieval algorithms, agents, data modeling for vector development graphs. Excellent communication and experience working with multi-functional teams

Responsibilities

The Machine Learning Engineer will drive the development and deployment of AI models and systems that enhance Apple's products and services. They will implement scalable ML infrastructure and develop automated ML pipelines for efficient data processing and model training.