Mô tả công việc
Company Description
Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial, infrastructure and IoT markets. Our robust product portfolio includes world- leading MCUs, SoCs, analog and power products, plus Winning Combination solutions that curate these complementary products. We are a key supplier to the world’s leading manufacturers of electronics you rely on every day; you may not see our products, but they are all around you.
Renesas employs roughly 21,000 people in more than 30 countries worldwide. As a global team, our employees actively embody the Renesas Culture, our guiding principles based on five key elements: Transparent, Agile, Global, Innovative, and Entrepreneurial. Renesas believes in, and has a commitment to, diversity and inclusion, with initiatives and a leadership team dedicated to its resources and values. At Renesas, we want to build a sustainable future where technology helps make our lives easier. Join us and build your future by being part of what’s next in electronics and the world.
About this role
As an AI/ML Engineer, you will design and develop scalable and reliable artificial intelligence and machine learning models for various domains and applications.
About our team
Our global AI, Data & Analytics Division at Renesas is experiencing an increasing demand for AI and Generative AI (LLM) based solutions. Working closely with our business partners, we deliver AI solutions to help drive value for Renesas at a global enterprise scale. We are seeking passionate candidates that will thrive as part of a global team of technologists that love empowering customers, collaborating with teammates, and using the latest AI and Data technologies.
Responsibilities
Train, evaluate, and deploy AI/ML models using Databricks MLflow
Develop AI and LLM solutions based on business requirements using approaches such as RAG architecture on enterprise data
Collaborate with data scientists and Full stack ai engineers, and domain experts on cross- functional projects using Databricks Workspaces
Prepare and augment training datasets for models
Research and evaluate new AI/ML technologies and Databricks best practices
