Full-Time Software Engineer - MLOps.

In this role, you will be part of the AI & Cloud Engineering (ACE) Division and MLOps team. We are developing a comprehensive AI strategy that delivers a highly flexible platform to explore new Deep Learning / Machine Learning model architectures, combined with auto-tuned high performance for production environments across a wide range of hardware architectures. The platform can improve performance, developer efficiency & deployment velocity of both AI training and inference.  

As an MLOps team member, you will develop the best-in-class software toolchain for AI software & hardware to support internal and external customers, which serves as the backbone of all products in our division. You will work closely with AI engineers to build innovative software tools to power the entire AI development lifecycle from developing and analyzing AI models to testing and loading the models on the hardware. You will then integrate the tools into a software platform delivered to our customers. You will apply software development best practices to design features, improve performance and deliver software. You will gain valuable experience in developing commercial grade MLOps products and will help in driving next generation hardware software co-design for AI domain specific problems.    

Our division’s mission is to use the latest AI and cloud technologies to develop the best AI inference for advanced driver safety engineers building self-driving vehicles and other high performance compute products. 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. You will join our newly formed AI & Cloud Engineering organization of around 100 software engineers. Due to strong demand for our AI-related products we are planning to triple in size in the next three years, so there is lots of room for you to help us grow the team together while remaining small. We are focusing on our hiring into our Tokyo, Beijing and Singapore sites. If you are successful and living outside of these cities, we can support your relocation to one of the three sites based on team needs. 

Responsibilities 

• Design the AI/ML pipelines and engineering infrastructure to support internal and external machine learning systems at scale.  
• Develop and deploy scalable tools and services to handle machine learning training and inference.  
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems.  
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.  
• Support model development, with an emphasis on auditability, versioning, and data security  
• Facilitate the development and deployment of proof-of-concept machine learning systems.  
• Communicate with clients to build requirements and track progress.  
• Bachelor’s or Master's degree in computer science, machine learning, mathematics, physics, electrical engineering or related field.  
• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)  
• Strong software engineering skills in complex, multi-language systems  
• Experience in C/C++, Python, or other related programming language  
• Experience working with cloud computing and database systems  
• Experience building custom integrations between cloud-based systems using APIs  
• Experience developing and maintaining ML systems built with open source tools  
• Experience developing with containers and Kubernetes in cloud computing environments  
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)  
• Understanding of software testing, benchmarking, and continuous integration  
• Experience working with machine learning frameworks such as PyTorch, TensorFlow, ONNX etc.  
• Understanding of System-on-Chip is a plus.  
  • Ability to speak and write in English at a business level.