苹果Machine Learning Engineer - Intern
任职要求
Minimum Qualifications • Currently pursuing a PhD degree or equivalent experience in Machine Learning, Computer Vision, Natural Language Processing, Data Science, Statistics or related areas. • Proven expertise in machine learning with a passion for data-centric machine learning. • Experience with natural language processing (NLP), and large language models, such as BERT, GPT, or Transformers. • Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax. Preferred Qualifications • Staying on top of emerging trends in LLMs • Strong problem-solving and communication skills • Demonstrated publication record in relevant conferences (e.g. NeurIPS, ICML, ICLR, CVPR, etc) is a plus • Available for 9+ months for internship
工作职责
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in ML to tackle complex data problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of a comprehensive data curation framework. You will also create robust model evaluation pipelines, integral to the continuous improvement and assessment of ML models. Additionally, your role will entail an in-depth analysis of collected data to underscore its influence on model performance. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. Your work may span a variety of topics, including but not limited to: * Designing and implementing semi-supervised, self-supervised representation learning techniques for maximizing the power of both limited labeled data and large-scale unlabeled data. * Developing evaluation protocols centered on the end-to-end user experience, with a focus on anticipating potential failure modes, edge cases, and anomalies. * Employing data selection techniques such as novelty detection, active learning, and core-set selection for diverse data types like images, 3D models, natural language, and audio. * Uncovering patterns in data, setting performance targets, and leveraging modern statistical and ML-based methods to model data distributions. This will aid in reducing redundancy and addressing out-of-distribution samples.
• 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.
• This role will involve in Software and Hardware R&D from initial phase, working on different wireless technologies(including 5G, LTE, NFC, GNSS technology, WiFi/Bluetooth, UWB) from design/prototype phase till finalization and post production/launch to end users. • You will work with our excellent Apple Engineering team on innovative project, in which you will be deeply involved into Product design, feature investigation, evaluation them in real world. • This includes to study and qualify relevant features at system level, from functional and performance perspective. Run focused experiments to understand and identify root cause of any issues and raise proposal of innovative ideas to influence the design from user experience point of view. • Assist to build up comprehensive database with various data from live network and real user scenarios • Work closely with Experienced Field Design Engineers and multi-functional teams to deep dive the data • Develop models based on Large-Scale data to predict potential issues, to support decision-making processes across teams
• Work with Apple engineers and manufacturing factory's engineering team on electrical tool development, failure analysis, HW testing and validation and other HW-related cross-team support • Travel to factories in other cities may be needed • The successful candidate will be involved in various aspects of HW development related to consumer electronics, gaining invaluable industry experience
An exciting internship opportunity to make an immediate contribution to AMD's next generation of technology innovations awaits you! We have a multifaceted, high-energy work environment filled with a diverse group of employees, and we provide outstanding opportunities for developing your career. During your internship, our programs provide the opportunity to collaborate with AMD leaders, receive one-on-one mentorship, attend amazing networking events, and much more. Being part of AMD means receiving hands-on experience that will give you a competitive edge. Together We Advance your career! JOB DETAILS: Location: Shanghai Onsite/Hybrid: This role requires the student to work full time (40 hours a week), either in a hybrid or onsite work structure throughout the duration of the co-op/intern term. Duration: January 1, 2026 - June 30, 2026 WHAT YOU WILL BE DOING: We are seeking highly motivated AI/ML Engineering Intern to join our AMD Research team. In this role: You will develop machine learning models to optimize GPU power/performance tradeoffs using real-world silicon data. We will train you to deploy models via MLOps pipelines for AMD’s internal tools. Your responsibility will include analyzing hardware telemetry data (power, thermal, clocks) to identify efficiency bottlenecks. You will collaborate with hardware engineers to validate models on next-gen AMD GPUs. Learning Outcomes: Master GPU-accelerated ML workflows. Gain hands-on experience with industrial-scale MLOps.