Deep Learning Compiler Engineer

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture. Quadric's co-optimized software and hardware is targeted to run neural network (NN) inference workloads in a wide variety of edge and endpoint devices, ranging from battery operated smart-sensor systems to high-performance automotive or autonomous vehicle systems. Unlike other NPUs or neural network accelerators in the industry today that can only accelerate a portion of a machine learning graph, the GPNPU is designed to accelerate the entire neural network graph, including both feedforward and recurrent neural networks. This allows for efficient processing of complex AI workloads such as computer vision, natural language processing, and reinforcement learning. The ideal candidate will have experience in deep learning compiler design, neural network architecture development, and software/hardware co-optimization. Proficiency in C++ programming is required, with Python or MATLAB being a plus. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Keras is also desired. The ability to work effectively in a collaborative environment and communicate complex technical ideas to both technical and non-technical stakeholders is essential.