Join our team as a Senior Software Engineer to develop and enhance sophisticated AI Developer Tools. These tools will be integrated via web APIs, SDKs, CLIs, and Agents, empowering rapid prototyping and advanced AI-assisted CUDA development. You will architect cloud solutions utilizing NVIDIA's microservices, frameworks, and cutting-edge AI applications to streamline the entire developer workflow, from initial coding to performance optimization.

This role offers an opportunity to architect cloud solutions that deeply integrate with developer workflows. You will leverage NVIDIA's microservices and ground-breaking AI research to accelerate the full product lifecycle, including planning, coding, testing, debugging, profiling, and fine-tuning.

NVIDIA is recognized as a highly desirable employer in the technology sector, boasting a team of forward-thinking and dedicated individuals. If you are passionate about empowering new programmers and enabling experienced accelerated computing users to redefine industries, this is the ideal opportunity for you.

Collaborate closely with Product and Design teams to define feature specifications and develop next-generation AI-assisted coding and profiling services.

Architect, design, and build high-performance, responsive SaaS solutions that enhance developer workflows using AI. Ensure high scalability, reliability, and cost efficiency to support numerous concurrent developers.

Coordinate with other engineering teams to align on corporate infrastructure strategies and contribute to the improvement and enhancement of existing services.

Provide mentorship to junior engineers and conduct code and design reviews.

Possess a B.S. in Computer Science or equivalent experience, coupled with at least 8 years of industry experience.

Demonstrate experience in developing large-scale, user-facing applications utilizing web and cloud services.

Familiarity with Kubernetes, Infrastructure as Code, and AWS is essential.

Experience with DevOps practices, including CI/CD, monitoring, and alerting, is required.

Proficiency in Python programming is a key requirement.

Exhibit strong communication skills to effectively collaborate with cross-functional partners.

Familiarity with either Linux virtualization for running isolated GPU workloads in the cloud or GPU profiling and benchmark performance in a repeatable manner is necessary.

Additional advantages include experience writing CUDA code and optimizing CUDA kernels, or experience with Kata Container or other VM-based container runtimes.