Instrument services with deep observability (metrics, logs, traces) to support rapid debugging and continuous performance improvement.
Build and own the model-serving infrastructure that operationalizes predictive algorithms at scale - packaging, versioning, deploying, and monitoring AI models in both SaaS and on-premises environments.
Contribute to the platform's core libraries and abstractions that accelerate development across the broader AIOps engineering team.
What we need to see
B.Sc./M.Sc. in Computer Science, Computer Engineering, or a related technical field.
8+ years of software engineering experience building production distributed systems.
Core Systems Programming: Expert-level proficiency in languages such as Go, C++, or Rust, with a focus on high-performance, concurrent architectures.
Solid understanding of Kubernetes and container-based deployments for production services.
Experience deploying, monitoring, and maintaining ML models or data-intensive services in a production environment.
Comfort working in ambiguous, fast-moving environments where the product is still being shaped.
Ways to stand out from the crowd
Experience building ML model-serving platforms or MLOps tooling (model registries, A/B rollout frameworks, feature stores) at scale.
A track record of taking systems from prototype to stable, production-grade platform serving real enterprise customers.
A "Systems" Thinker: You don't just write software; you understand the full stack, from how data moves across the wire to how it’s processed in a distributed cluster.
Practical Innovation: The ability to simplify complex problems and build internal tools or frameworks that empower other engineering teams to move faster.
With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you are passionate about building mission-critical systems at the frontier of AI infrastructure, we want to hear from you.
