【Apple】Machine Learning Engineer, Ai & Data Platform
仕事内容
https://jobs.apple.com/en-us/details/200588535/machine-learning-engineer-ai-data-platform?team=CORSV
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.
応募資格(必須経験など)
Minimum Qualifications
• Strong proficiency in programming languages like Java or Python
• Solid understanding of Data Structures and Algorithms.
• 3+ years of machine learning engineering experience in feature engineering, model training, model serving, model monitoring, and model refresh management.
• 1+ 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