What are the responsibilities and job description for the Machine Learning Engineer, Apple Search & Knowledge Platforms position at Apple, Inc.?
The Apple Knowledge Quality Team is building the next-generation of machine learning solutions for Knowledge Q&A at Apple and help power features including Siri and Spotlight. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for. As part of this group, you will work with one of the most exciting high performance computing environments, with petabytes of data, millions of queries per second, and have an opportunity to imagine and build products that delight our customers every single day.
The Knowledge Quality team is looking for extraordinary Machine Learning engineers to join a team of world-experts on Large-Scale Data Management and Machine Learning Systems. Together, you will be pushing the boundaries of Knowledge Question Answering in Siri. As part of the Knowledge Quality team, you will design and develop features for a platform that touches upon large-scale data management, machine-learning and deep learning systems over graph data and web documents. You will have the outstanding opportunity to inform product evolution through measurement, evaluation, and analysis of the user experience. You will partner with cross-functional teams to change how hundreds of millions of people use their computers and mobile devices to search and provide users with results that best satisfy their information seeking needs.
MS degree in Computer Science, Machine Learning, or related field with 2 years of industry experience building production ML/AI systems, OR PhD degree in a related field\nProficiency in mainstream programming languages such as Python, Scala, and Go\nExperience building and maintaining large-scale data systems, knowledge graphs, and end-to-end ML pipelines in production, ideally using the Apache software stack (e.g., Spark)\nHands-on experience with machine learning frameworks such as PyTorch or TensorFlow in production environments\nExperience with natural language processing, statistical data analysis, and model evaluation methodologies\nDemonstrated ability to collaborate with cross-functional teams including product, engineering, and data science\nExperience with CI/CD pipelines, model deployment, and monitoring solutions
MS degree with 6 years of industry experience building and scaling ML/AI systems, OR PhD degree with 3 years of industry experience in production ML environments\nProven track record designing, deploying, and maintaining large-scale distributed ML systems serving millions of QPS (queries per second)\nExperience with A/B testing, experimentation frameworks, and data-driven product iteration at scale\nExperience designing human-in-the-loop evaluation pipelines and leveraging user feedback to improve model performance\nHands-on experience with LLM deployment, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), or other generative AI technologies in production\nExperience building model monitoring, observability, and quality assurance systems for production ML services\nExperience optimizing ML systems for latency, throughput, and cost at scale\nTrack record of shipping ML-powered features that measurably improved user experience for consumer-facing products\nStrong product intuition and ability to translate business requirements into technical solutions
The Knowledge Quality team is looking for extraordinary Machine Learning engineers to join a team of world-experts on Large-Scale Data Management and Machine Learning Systems. Together, you will be pushing the boundaries of Knowledge Question Answering in Siri. As part of the Knowledge Quality team, you will design and develop features for a platform that touches upon large-scale data management, machine-learning and deep learning systems over graph data and web documents. You will have the outstanding opportunity to inform product evolution through measurement, evaluation, and analysis of the user experience. You will partner with cross-functional teams to change how hundreds of millions of people use their computers and mobile devices to search and provide users with results that best satisfy their information seeking needs.
MS degree in Computer Science, Machine Learning, or related field with 2 years of industry experience building production ML/AI systems, OR PhD degree in a related field\nProficiency in mainstream programming languages such as Python, Scala, and Go\nExperience building and maintaining large-scale data systems, knowledge graphs, and end-to-end ML pipelines in production, ideally using the Apache software stack (e.g., Spark)\nHands-on experience with machine learning frameworks such as PyTorch or TensorFlow in production environments\nExperience with natural language processing, statistical data analysis, and model evaluation methodologies\nDemonstrated ability to collaborate with cross-functional teams including product, engineering, and data science\nExperience with CI/CD pipelines, model deployment, and monitoring solutions
MS degree with 6 years of industry experience building and scaling ML/AI systems, OR PhD degree with 3 years of industry experience in production ML environments\nProven track record designing, deploying, and maintaining large-scale distributed ML systems serving millions of QPS (queries per second)\nExperience with A/B testing, experimentation frameworks, and data-driven product iteration at scale\nExperience designing human-in-the-loop evaluation pipelines and leveraging user feedback to improve model performance\nHands-on experience with LLM deployment, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), or other generative AI technologies in production\nExperience building model monitoring, observability, and quality assurance systems for production ML services\nExperience optimizing ML systems for latency, throughput, and cost at scale\nTrack record of shipping ML-powered features that measurably improved user experience for consumer-facing products\nStrong product intuition and ability to translate business requirements into technical solutions