NVIDIA is looking to engineer new paradigms in productivity infrastructure and platforms by leveraging AI-accelerated programmers to modernize the silicon engineering stack and supercharge chip design engineers' work.
Requirements
- Proficiency in thought-to-code and rapid understanding of existing or AI-generated code to debug and identify pathways to improve performance, resilience and observability.
- Minimum 5 years developing large-scale software applications in an enterprise engineering environment using Python
- Internalized computer science fundamentals in algorithms/data structures/complexity analyses
- Rich history of wrangling LLMs (GPT 4o through Claude Opus 4.x) to ship real software
- Deep, demonstrable intuition of the capabilities and pitfalls of LLM code generation ("vibe coding")
- Track record of shipping and supporting AI-generated code into enterprise engineering applications
- Solid fundamentals of UNIX internals, filesystems, job-scheduling, processes, synchronization, and locks
Responsibilities
- Design, develop and deliver core components of our next-generation engineer productivity platforms.
- Envision, build, deploy and improve highly scalable systems at the speed of thought
- Disambiguate high-level requirements into actionable Agentic AI tasks with clear vetting and ownership of output
- Jump between microservices and monoliths, on-prem and cloud-compute as needed to build efficient products
- Convert legacy codebases into modern powerhouses with parallelized AI migration
- Collaborate with engineering teams to identify and alleviate bottlenecks in their daily tasks
- Be adept and flexible to changing priorities and updating your codebases as business needs evolve
Other
- M.S. in EE/CS (or equivalent experience)
- Experience with Perforce, NFS, LSF and running services on managed infrastructure without root/sudo
- Experience building maintainable software using off-the-shelf tools and open-source libraries to solve feature gaps quickly
- You've handled your own deployment and infrastructure on a cloud provider and on bare-metal servers
- Rapid problem decomposition and clear communication when delegating work to AI Agents
