● MS or PhD with published research work in ML model optimization, post-training quantization, consideration of different datasets and different constrains (bit-accuracy, model size, latency and so on).
● Experience with popular ML frameworks, such as PyTorch and TensorFlow
● Startup mindset/experience
Experience in one or more of the following areas considered a strong plus:
● Experience with popular light-weight ML models on edge inference
● Hands-on experiences with deploying/evaluating ML models on resource/power-limited computing platforms.
● Experience providing technical leadership and/or guidance to other engineers
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