Sr. Staff AI Engineer

General Information

Job Title Sr. Staff AI Engineer Job ID 8217 City Sunnyvale State/Province California Date Posted 09-Dec-2024 Job Category Engineering Job Subcategory AI Hire Type Employee Remote Eligible No Base Salary Range: $184000 - $276000

Descriptions & Requirements

Job Description and Requirements

We Are:

At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future through continuous technological innovation.

You Are:

A passionate and experienced AI engineer with a strong background in building and managing AI and GenAI platforms. You have a deep understanding of cloud technologies, Large Language Models(LLMs), Inferencing infrastructure, containerization, and microservices. With over 10 years of experience, you excel in designing complex distributed systems and have a keen ability to solve intricate problems using efficient algorithms. You thrive in a collaborative environment and are excited about the opportunity to lead and innovate in the semiconductor industry. Your expertise in programming languages like Python and Go, coupled with your knowledge of Kubernetes and cloud platforms, makes you a perfect fit for this role. You are enthusiastic about leveraging AI to enhance R&D efforts and are committed to continuous learning and professional development.

What You’ll Be Doing:

• Designing and building GenAI environments for our R&D products to integrate and experiment with a wide variety of Large Language Models (LLMs) and inferencing technologies (e.g., TGI, TEI, NIMs) hosted on-premises using Docker and Kubernetes.  
• Developing an ecosystem to enable R&D teams to host Gen AI applications in the Cloud.
• Creating capabilities to ship Cloud Native (Containerized) AI applications/AI systems to on-premises customers.
• Orchestrating GPU scheduling from within the Kubernetes ecosystem (e.g., Nvidia GPU Operator,