LLM Machine Learning Research Engineer

Summary

Apple is seeking a Research Engineer to join our Foundation Model Preparation and Algorithm Team. We are looking for all levels of talent to bring innovative AI research into Apple products.

Description

We are looking for strong ML applied scientists and engineers to build groundbreaking AI infrastructure and algorithms. This infrastructure will power the optimization of Apple Foundation models, including on-device and server Apple Intelligence models. My team is directly responsible for general model capability, use-case-oriented post-training, and also the feature delivery for Apple Intelligence. You should be a strong scientist and/or engineer who has a background in building state-of-the-art LLMs. Your work will have a direct impact on billions of Apple clients. You will collaborate with world-class talent in LLM training, on-device and server optimization, ML tools/platforms, datasets, and evaluation. You will develop reliable and scalable pipelines and algorithms, such as:Model optimization pipelines, State-of-the-art optimization algorithms, State-of-the-art post-training techniques.

Minimum Qualifications

Experience developing, optimizing, or training large language models (LLMs), large foundation models, or generative AI models. Software engineering skills in Python and general-purpose system administration and infrastructure management abilities. History of applied research in the neural network model life cycle, training, or a related application area. Experience with languages like Python, C/C++. Track record of driving scientific investigations and experiments, and overcoming obstacles and uncertainty in a research environment. BS degree and 3+ years of proven experience.

Preferred Qualifications

Publication record at top AI/ML venues. Experience with LLM LoRA fine-tuning, neural network optimization (e.g., quantization, palettization). Experience with LLM pre-training or post-training. Experience with on-device/server scale deployment. Infrastructure management and debugging experience. Experimental rigor when training/evaluating LLMs for the purpose of benchmarking LLM optimization algorithms. Strong communication and accountability skills; a hard-working, strong work ethic, and collaboration abilities. Ph.D. in a related field.