Job Description
In this role, you will be part of the Machine Learning Core team. The team has been developing a comprehensive AI Compiler strategy that delivers a highly flexible platform to explore new DL/ML model architectures, combined with auto-tuned high performance for production environments across a wide range of hardware architectures. The compiler framework, ML graph optimizations and kernel authoring specific to the hardware impacts performance, developer efficiency & deployment velocity of both AI training and inference platforms. You will be developing AI compiler frameworks to accelerate machine learning workloads on the next generation of AI hardware. You will work closely with AI researchers to analyze deep learning models and how to lower them efficiently on AI platforms. You will also partner with hardware design teams to develop compiler optimizations for high performance. You will apply software development best practices to design features, optimization, and performance tuning techniques. You will gain valuable experience in developing machine learning compiler frameworks and will help in driving next generation hardware software co-design for AI domain specific problems.
Our mission is to use the latest machine learning and cloud technologies to develop the best AI inference for self-driving vehicle and advanced driver safety engineers. Renesas is the leading automotive electronics supplier globally, and this is a rare opportunity to deploy your AI software to the billions of devices we ship to customers every year.
Qualifications
- Bachelor’s or Master’s degree in computer science, machine learning, mathematics, physics, electrical engineering or related field.
- Experience in C/C++, Python, or other related programming language
- Experience in accelerating deep learning models or libraries on hardware architectures.
- Experience working with machine learning frameworks such as PyTorch, TensorFlow, ONNX etc.
