AIML - Machine Learning Engineer (Model Optimization Library, On-Device Machine Learning), Machine Learning Platform & Technology
Cupertino,California,United States
Machine Learning and AI
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Submit Resume AIML - Machine Learning Engineer (Model Optimization Library, On-Device Machine Learning), Machine Learning Platform & Technology
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Summary
Posted:Mar 5, 2024
Weekly Hours: 40
Role Number: 200540879
Are you excited about the impact that optimizing deep learning models can have on enabling transformative user experiences? The field of ML compression research continues to grow rapidly and new techniques to perform quantization, pruning etc are increasingly available to be ported and adopted by the ML developer community looking to ship more models in a constrained memory budget and make them run faster. We are passionate about productizing and pushing the envelope of the state of the art model optimization algorithms, to further compress and speed up the thousands of deep learning models shipping as part of Apple internal and external apps, running locally on millions of Apple devices. We work on a python library that implements a variety of training time and post training quantization algorithms and provides them to developers as simple to use, turnkey APIs, and ensures that these optimizations work seamlessly with the Core ML inference stack and Apple hardware. We are a small core team that collaborates heavily with researchers at Apple, ML software and hardware architecture teams and external product teams shipping state of the art optimized models on Apple devices. If you are excited about making a big impact and playing a critical role in growing the user base and driving the adoption of a relatively new library, this is a great opportunity for you. We are looking for someone who is highly self motivated and passionate about optimizing models for on device execution. If you have a proven track record of applied deep learning research in model quantization, writing high quality code and shipping software, we strongly encourage you to apply.
Key Qualifications
A proven track record of applied research excellence via presentations, demos, or publications at leading ML conference or product development and deployment experience or other artifacts such as software projects.
Excellent knowledge of theory and practice of ML.
Experience in the area of model compression and quantization techniques.
Expertise with programming systems such as Python, deep learning frameworks such as PyTorch, JAX or TensorFlow.
Proven record of training, fine tuning, optimizing models
Strong programming skills.
Excellent technical communications skills
Demonstrated ability to design user friendly and maintainable APIs
Description
We work on developing, prototyping and productizing state of the art algorithms for neural network model compression. Our algorithms are implemented using PyTorch and optimizations are geared towards efficient deployment via Core ML. We optimize models across domains, including NLP, vision, text and large generative models. Our APIs are available to Core ML users, both internal to Apple and external developers via the Core ML Tools optimization sub module. As a successful engineer in our team, you will: * Implement latest algorithms from research papers for model compression and apply to latest model architectures such as diffusion models, large language models etc * Set up training jobs, datasets, evaluation, performance benchmarking pipelines. Applying training time and post training compression techniques * Understanding HW capabilities and incorporating those in optimization algorithm design / enhancement * Build upon the latest research to create new algorithms and invent new ways of applying compression to deep learning models from different domains * Keep up with the latest AI research and collaborate with diverse teams, both internal and external to Apple, including researchers, hardware architects, and software engineers, to co develop and implement algorithms customized for Apple hardware * Run detailed experiments and ablation studies to profile algorithms on various models, tasks, across different model sizes * Improving model optimization documentation, writing tutorials and guides * Self prioritize and adjust to changing priorities and asks
Education & Experience
Additional Requirements
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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.
Pay & Benefits
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Apple is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities. Apple is a drug-free workplace. Reasonable Accommodation and Drug Free Workplace policy Learn more (Opens in a new window) .
