Nvidia Principal Software Engineer 

United States, California 

240495405

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

What you'll be doing:

• Design and build software platforms that transform legacy database systems into modern, containerized, and scalable architectures.

• Run vector database services and query engines to handle AI/ML data workloads with ultra-low latency.

• Create automation frameworks for provisioning, schema evolution, scaling, and failover—integrated directly into CI/CD workflows.

• Collaborate with AI/ML and application engineers to design optimized data models, APIs, and query patterns for large-scale training and inference pipelines.

• Build developer-focused tooling for monitoring, profiling, and debugging database performance in real time.

• Implement secure-by-design database services with enterprise-grade identity, access control, and secrets management.

• Prototype and evaluate new database technologies, query accelerators, and storage engines to push the boundaries of performance.

• Contribute to architecture, coding standards, and best practices for long-term platform evolution.

What we need to see:

• 15+ years of software engineering experience with deep expertise in database systems or distributed data platforms.

• Bachelor's degree in information security, IT, Compliance, or a related field, or equivalent experience (Master’s preferred).

• Strong programming skills in Python, Go, C++, or Java, with a track record of building production-grade systems.

• Proven experience designing high-performance, high-availability relational database services (PostgreSQL or equivalent).

• Experience with container orchestration (Kubernetes) and cloud-native database deployment patterns.

• Strong background in query optimization, data partitioning, and large-scale performance tuning.

• Hands-on experience integrating database services into CI/CD pipelines.

Ways to stand out from the crowd:

• Designed or contributed to GPU-accelerated query engines or real-time analytics platforms.

• Open-source contributions in the database, distributed systems, or AI/ML infrastructure space.

• Expertise in hybrid/multi-region database replication strategies for low-latency AI workloads.

• Strong understanding of observability and performance profiling tools for complex data systems.

• Experience in building platforms that directly support AI/ML research and production deployment.