Machine Learning Research Engineer, LLM

** Role Number:*
* ** Summary*
* Apple is seeking a Machine Learning 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.

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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.

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Experience with LLM LoRA fine-tuning, neural network optimization (e.g., quantization, palettization).

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Experience with LLM pre-training or post-training.

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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.