Senior Staff Software Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Staff Software Engineer role focused on building and developing GPU-accelerated platforms and software systems for large-scale AI workloads, including transforming legacy databases and running low-latency vector/graph database services.

What you'd actually do

  1. Compose and build software platforms that transform legacy database systems into modern and scalable architectures.
  2. Implement secure-by-design database services with enterprise-grade identity, access control, and secrets management.
  3. Run vector & graph database services and query engines to handle AI/ML data workloads with ultra-low latency.
  4. Build automation frameworks for provisioning, schema evolution, scaling, and failover integrated directly into CI/CD workflows.
  5. Build developer-focused tooling for monitoring, profiling, and debugging database performance in real time.

Skills

Required

  • Python
  • Go
  • C++
  • Java
  • database systems
  • distributed data platforms
  • relational database services
  • container orchestration
  • Kubernetes
  • cloud-native database deployment patterns
  • query optimization
  • data partitioning
  • large-scale performance tuning
  • DevOps
  • CI/CD
  • monitoring
  • alerting
  • SLAs
  • capacity forecasting
  • security awareness
  • authentication
  • data compliance

Nice to have

  • Open-source contributions in the database, distributed systems, or AI/ML infrastructure space
  • hybrid/multi-region database replication strategies
  • observability
  • performance profiling tools
  • building platforms that directly support AI/ML research and production deployment

What the JD emphasized

  • 12+ years of software engineering experience
  • deep expertise in database systems or distributed data platforms
  • track record of building production-grade systems
  • Proven experience crafting high-performance, high-availability relational database services
  • Strong background in query optimization, data partitioning, and large-scale performance tuning
  • Hands-on experience establishing DevOps guidelines
  • Strong security awareness
  • low-latency AI workloads

Other signals

  • GPU-accelerated platforms
  • AI workloads at scale
  • vector & graph database services
  • low-latency AI workloads