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 team that collaborates heavily with researchers at Apple, ML software and hardware architecture teams and external/internal 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 looking for an opportunity to lead the testing and automation initiatives for a model optimization library for on device execution. If you are someone who is passionate about maintaining a high code quality and testability of production code, have experience setting up and maintaining CI pipelines for software projects, we strongly encourage you to apply.
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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 image generative models etc. Key responsibilities of this role are: * Setting up, and/or streamlining CI and automation pipelines. Adopting the best practices and integrating with the latest Apple internal CI services for the same. * Making enhancements to the release process, automating nightly builds, setting up scheduled CI runs for different levels of testing etc. * Making innovations in model testing and benchmarking (accuracy and latency), for various combinations of model types in different domains (vision, text, audio etc) and compression algorithms (quantization, pruning, palettization etc), discovering trends, effects of various hyper parameters etc. * Be passionate about engineering efficiency, finding innovative ways to reduce test time while maintaining a high bar of test coverage * Obsess about user experience and improving it. You are someone who is excited to fix bugs, understand user pain points and actively participates in supporting the users. * Developing integration of the model optimization library with other training engines and data platforms at Apple. * Keeping the code base updated to work with the latest versions of Python, PyTorch, numpy etc. * Set up and debug training jobs, datasets, evaluation, performance benchmarking pipelines. Ability to ramp up quickly on new training code bases and run experiments. 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
Minimum Qualifications
- BS/MS in Computer Science or related field
- Relevant internship experience
Preferred Qualifications
- Demonstrated ability to design user friendly and maintainable APIs
- Proficiency in at least one ML authoring framework, such as PyTorch, TensorFlow, JAX, MLX
- Experience in training, fine tuning, and optimizing neural network models
- Experience in the area of model compression and quantization techniques, specially in one of the optimization libraries for an ML framework (e.g. torch.ao).
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $121,900 and $183,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote 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. Learn more about your EEO rights as an applicant .
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