**Company: Apple Job Description Summary: The text is a job posting from Apple for a ML Engineer position in the Human and Object Understanding team. The role involves working with large-language models and multi-modal generative models to build adapters and enable specific use cases across the Apple ecosystem. The job requires familiarity with Python, PyTorch, TensorFlow, and experience with LLM-based workflows and multi-modal settings. Preferred qualifications include a Master’s or Ph.D. in Computer Science, Artificial Intelligence, or Machine Learning. Apple is an equal-opportunity employer committed to inclusion and diversity. Job Description: Summary Posted: Jun 6, 2024 Weekly Hours: 40 Role Number:200509737 We work on the cutting edge of Artificial Intelligence and Machine learning to build intelligent system experiences for the world’s most impactful platforms such as iOS, macOS, tvOS, etc. This system-wide intelligence aims to provide best-in-class solutions for problems that are critical to the success of 1st and 3rd party applications in Apple platforms. Some examples of such areas include sharing suggestions, vector indexing and search, discovering and indexing people identities, social relationships, visual recognition of people and things, OCR, natural language generation, visual generation, etc. We are looking for highly skilled and creative ML practitioners who are well versed with using large language models (LLMs) for a variety of downstream tasks beyond language generation. Of particular interest is using LLMs to reason in a multi-modal setting, by combining imperfect visual perception with contextual… information derived from the system. We are the Human and Object Understanding (HOUr) team within the System Intelligence and Machine Learning (SIML) group. We are an applied R&D team that develops fundamental ML technologies and systems for visual perception and reasoning of humans-in-context. Some examples of visual perception technologies the team own include real-time always-on object detection (Center Stage, Cinematic Mode), end-end system-wide person recognition (Photos, HomeKit, Memojis, Apple Pay), spoof detection (IDs in Wallet), and gaze understanding (Center Stage, intelligent cropping). Some examples of high-level reasoning systems include: sharing suggestions, inferring name-person relationships, and efficient vector indexing. Description Description As a ML engineer in the SIML HOUr team, you will work with large-language models and multi-modal generative models, following closely groundbreaking advancements in this domain, to adjust and apply them to internal use cases. One main mission of the role is building adapters on top of large models to enable specific use cases, having a direct impact on features across the Apple ecosystem. The work will involve translating high-level product goals into different levels of the stack. From defining the data needs, manipulating data, fine-tuning pre-trained models for the task, evaluating it across relevant metrics and power and performance, prototyping and delivering it for integration. The work will be multi-functional, collaborating with ML researchers, software engineers, product design, and other teams. Be expected to iterate quickly to deliver a high quality model, that is performant, reliable, tested extensively, and documented. Apart from model development, the role will also give the experience of scoping projects, estimating timelines, multi-functional planning and presenting your work to organization leadership. If this could be of interest, please apply! Minimum Qualifications Minimum Qualifications Key Qualifications Key Qualifications • Familiarity with Python, PyTorch, TensorFlow • Hold yourself and others to a high bar when delivering a model • Have great communication skills, for collaborating across many participating teams • Hands-on experience with LLM-based workflows: prompt engineering and parameter-efficient fine-tuning of pre-trained LLM • Experience with multi-modal setting, specifically Vision and Language • Ability to rapidly iterate with fine-tuning toolboxes. • Ability to translate high-level product goals into data, model and metrics requirements. • Awareness and attention to model complexity, power and performance. Preferred Qualifications Preferred Qualifications Education & Experience Education & Experience Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field Additional Requirements Additional Requirements • Apple’s most important resource, our soul, is our people. Apple benefits help further the well-being of our employees and their families in meaningful ways. No matter where you work at Apple, you can take advantage of our health and wellness resources and time-away programs. We’re proud to provide stock grants to employees at all levels of the company, and we also give employees the option to buy Apple stock at a discount — both offer everyone at Apple the chance to share in the company’s success. You’ll discover many more benefits of working at Apple, such as programs that match your charitable contributions, reimburse you for continuing your education and give you special employee pricing on Apple products. • Apple benefits programs vary by country and are subject to eligibility requirements. • Apple is an equal-opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Apple is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities. Apple is a drug-free workplace. More
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Level of experience (years):Senior (5+ years of experience)
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
Hire an NLP developer and untangle the power of natural language in your projects The world is buzzing with the possibilities of natural language processing (NLP). From chatbots that understand your needs to algorithms that analyse mountains of text data, NLP is revolutionising industries across the board. But harnessing this power requires the right expertise. That’s where finding the perfect NLP developer comes in. Post a job in NLP on naturallanguageprocessing.
Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.