Staff Engineer, Performance Modelling

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.

We are seeking a highly skilled and experienced Engineers to join our team.

The ideal candidate will be responsible for developing performance models using SystemC TLM 2.0 and performance analysis of complex SoCs.

The role involves working closely with SoC architects and cross-functional teams to optimize SoC performance and ensure the successful delivery of high-quality products.

• Develop and maintain performance models for SoC designs using SystemC TLM 2.0
• Collaborate with architecture, design, software and verification teams to define performance requirements and ensure alignment with overall system goals
• Analyze and optimize system performance, including DDR, CPU, GPU, Interconnects and high-speed interface like PCIe, UCIe etc
• Identify performance bottlenecks and propose solutions to improve system efficiency