苹果Machine Learning Engineer

LLM Application in Customer Service: Design, develop, and iterate on prompts for various LLM applications within the customer service center, including conversational AI, content generation, and summarization. Experiment with prompt formats, styles, and techniques to optimize LLM performance and output quality. Analyze LLM-generated responses, identify biases or limitations, and implement mitigation strategies to enhance customer satisfaction and service performance. Operational Excellence: Identify and propose innovative projects that leverage LLMs to solve complex problems or explore new capabilities in customer service. Conduct research, experiment with cutting-edge techniques, and develop prototypes for potential products or services. Collaborate with data scientists, machine learning engineers, and other stakeholders to integrate prompts and research findings into the broader AI/ML pipeline. Performance Analysis: Analyze the content of customer interactions to generate self-service rate analysis reports, explaining why customers transfer to agents and what can be done to improve these indicators. Generate regular chat-bot performance reports and continuously improve the algorithm model accuracy, customer satisfaction, and service performance of the chat-bot. Team Collaboration: Work closely with relevant function teams to deliver an integrated workflow of demand negotiations, standard creations, and project implementations. Share knowledge and best practices with the team, mentor junior AI operation peer, and contribute to a collaborative learning environment.

Job Description： 1.Optimize the recommendation quality and user profile in Mi.com website, provide users the best shopping experience； 2.Combine your understanding of product objectives and take full advantage of modern machine learning, NLP and Multimodal techniques to improve the recommendation result metrics； 3.Work with products and DAs, and other engineers to deliver features to drive the experience optimization of products.

As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying innovative research in foundation models to with a particular focus on audio data. This includes working across the full ML pipeline—from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning with task-specific datasets. The solutions you develop will have a significant impact on future Apple software and hardware products, as well as the broader ML ecosystem. Your responsibilities will extend to designing and developing a comprehensive multi-modal data generation and curation framework for foundation models at Apple. You will also contribute to building robust model evaluation pipelines that support continuous improvement and performance assessment. In addition, the role involves analyzing multi-modal data to better understand its influence on model behavior and outcomes. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. YOUR WORK MAY SPAN VARIOUS APPLICATIONS, INCLUDING: Designing self-supervised and semi-supervised representation learning pipelines, and fine-tuning strategies for tasks like speech recognition and speaker identification. Applying data selection techniques such as novelty detection and active learning across multi modalities to improve data efficiency and reduce distributional gaps. Modeling data distributions using ML/statistical methods to uncover patterns, reduce redundancy, and handle out-of-distribution challenges. Rapidly learning new methods and domains as needed, and guiding product teams in selecting effective ML solutions.

• Candidate will be responsible for: • - Collaborate with senior engineers to design and develop ML tools for data processing, analysis, and visualization. • - Integrate and validate machine learning models on Apple new product introduction (NPI) process and mass production. • - Develop and implement automated pipelines for model training and evaluation. • - Conduct experiments to evaluate the effectiveness of different models and algorithms. • - Participate in code reviews, testing, and documentation to ensure high-quality and maintainable code. • - Keep up-to-date with the latest advancements in machine learning and data science techniques.