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. Our algorithms are implemented using PyTorch. We optimize models across domains including NLP vision text generative models this role the Model Optimization Engineer will be an expert in understanding the internal workings of PyTorch graph capturing and graph editing mechanisms methods to observe and modify intermediate activations and weights tensor subclasses custom ops different types of parallelism for training models and use this knowledge to implement and update the core infrastructure of the optimization library which enables an efficient and scalable implementation of various classes of compression algorithms. Youll also set up and debug training jobs datasets evaluation performance benchmarking pipelines. Additionally you will...- Design and develop the core infrastructure which powers the implementations of various compression algorithms (training time post training data free calibration data based etc)- Implement the latest algorithms from research papers for model compression in the optimization library.- Collaborate with software and hardware engineers from the ML compiler inference stack to co-develop new compression operations and model export flows for on device deployment. - Design clean intuitive maintainable APIs - Run detailed experiments and ablation studies to profile algorithms on various models and tasks across different model sizes.
Bachelors in Computer Sciences Engineering or related discipline.
3 years of industry and/or research experience
Highly proficient in Python programming
Proficiency in at least one ML authoring framework such as PyTorch TensorFlow JAX MLX
Experience in the area of model compression and quantization techniques specially in one of the optimization libraries for an ML framework (e.g. ).
Ability to ramp up quickly on new training code bases and run experiments.
Demonstrated ability to design user friendly and maintainable APIs
Experience in training fine tuning and optimizing neural network models
Primary contributor to a model optimization/compression library.
Self prioritize and adjust to changing priorities and asks
Improving model optimization documentation writing tutorials and guides
Good communication skills including ability to communicate with cross-functional audiences
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 $147400 and $272100 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 Apples 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 Apples Employee Stock Purchase Plan. Youll 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.
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