6 hours ago
Shanghai, ChinaMid Level
Responsibilities
- Build infrastructure and pipelines that transform simulation data on power, noise, and binning yields into firmware tuning, product specifications, and manufacturing limits.
- Connect simulation, firmware, manufacturing, and specification tools across the silicon productization workflow.
- Apply LLMs and agents to automate engineering analysis, validation, and reporting.
- Build observability, automated checks, schema validation, and integration tests to detect data errors and inconsistencies.
- Work with product convergence, silicon architecture, firmware, and manufacturing teams to translate hardware requirements into production workflows.
- Support production-critical systems whose users may require assistance outside normal business hours.
Requirements
- BS or MS in computer science, computer engineering, electrical engineering, or systems engineering, or equivalent experience.
- 4+ years of experience in a related hardware/software position.
- Strong understanding of digital design, circuit analysis, algorithms, computer architecture, silicon speed and power, BIOS, drivers, and software applications.
- Experience with Perl or Python, databases, and web applications.
- Strong software algorithm and object-oriented programming fundamentals.
- Hands-on experience applying LLMs to engineering problems, including agents, MCP, RAG, or evaluation pipelines.
- Experience shipping an LLM-backed feature in production and debugging it.
- Strong data-quality practices involving automated checks, schema validation, and integration tests.
- Preferred experience with silicon product characteristics such as speed, power, voltage noise, and binning; MCP, DSPy, or LLM evaluation frameworks; Perl interoperability; and dashboards or visualizations.
Tech Stack
PerlPython
Categories
AI ApplicationsData Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.