Software Engineer (AML), AI & Data Platforms (AiDP) at Apple

Austin, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

04 Sep, 26

Salary

0.0

Posted On

06 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Engineering, Feature Engineering, Model Serving, PyTorch, TensorFlow, Python, SQL, Generative AI, Large Language Models, Computer Vision, Natural Language Processing, Model Compression, Anomaly Detection, Forecasting, Data Visualization, Software Engineering

Industry

Computers and Electronics Manufacturing

Description

Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here. AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple. DESCRIPTION As a Machine Learning Engineer, you will work on building intelligent systems to democratize AI across a wide range of solutions within Apple. You will drive the development and deployment of innovative 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 BS in Computer Science or related field or equivalent. 3+ years of machine learning engineering experience in feature engineering, model training, model serving, model monitoring and model refresh management. Experience developing AI/ML systems at scale in production or in high-impact research environments. Passionate about computer vision, natural language processing, especially in LLMs and Generative AI systems. Knowledge with the common frameworks and tools such as PyTorch or TensorFlow. PREFERRED QUALIFICATIONS Experience in Anomaly detection and forecasting & related methodologies Experience and proficiency in python & writing efficient SQLs. Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment. Proven experience with transformer models such as BERT, GPT etc., and a proven understanding of their underlying principles is a plus Data Visualization Tools: Proficient in data visualization, with experience in software such as Superset, Streamlit, Tableau, Business Objects, and Looker

Responsibilities

Develop and deploy scalable AI models and ML infrastructure to democratize AI across Apple's internal solutions. Design automated ML pipelines for data preprocessing, training, and evaluation while optimizing language models for performance.