Embedded Machine Learning (ML) Engineer
- Optimize neural networks to run efficiently on embedded devices
- Evaluate neural network hardware accelerators, including working with the various vendors' custom hardware stacks
- Research and evaluate methods to run neural networks more efficiently, such as quantization or model pruning
- Share your knowledge about efficient ML with the broader deep learning R&D team, to help us design networks and algorithms that are more efficient on low-power platforms Knowledge, Skills, and Abilities:
- Experience in industry or academia experience focused on efficient deep learning
- Experience with at least one neural network inference framework such as TFLite, ONNXRuntime, or TVM, including some understanding of how they work internally
- Experience with topics such as neural network quantization, pruning, or compression
- Strong programming skills in C++
- Experience with SIMD programming or other optimization techniques is a plus
- Experience with neural network accelerator ASICs is a plus
- Bachelor's degree in CS, EE, or equivalent
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