Requirements:

●     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