Silicon Performance, Power and Binning Tools Engineer
NVIDIAJob Title
Silicon Performance, Power and Binning Tools Engineer
Role Summary
Work on NVIDIA's silicon productization toolchain to convert simulation outputs (power, noise, binning yields, etc.) into firmware tuning, product specifications, and manufacturing limits. The team builds pipelines, validation, and analysis tooling and is rebuilding the toolchain around AI.
The role collaborates with silicon architecture, firmware, manufacturing, and product teams to deliver reliable data pipelines and automation that feed production systems.
Experience Level
Mid-level (4+ years of relevant hardware/software experience)
Responsibilities
Primary responsibilities focus on building and maintaining pipelines, automation, and validation systems that move chip simulation data into production systems.
- Design and own pipelines that transform raw simulation data into firmware tuning, product specs, and manufacturing limits.
- Apply LLMs and agents to automate analysis, validation, and reporting across the toolchain.
- Implement observability, schema validation, and integration tests to detect data errors before release.
- Integrate new hardware requirements and capabilities into production workflows with cross-functional teams.
Requirements
Core qualifications and technical skills required. Candidates who meet these should be able to work across legacy chip-data workflows and modern AI-backed pipelines.
- 4+ years in a related hardware/software role working with silicon or system-level tooling.
- Strong understanding of digital design, circuit analysis, algorithms, computer architecture, silicon speed and power, BIOS, drivers, and application software.
- Proficiency with scripting and engineering tooling (Perl or Python), databases, and web applications.
- Solid software fundamentals: algorithms, object-oriented programming, and decomposition of complex problems.
- Hands-on experience shipping LLM-backed engineering features (agents, MCP, RAG, evaluation pipelines) and debugging them in production.
- Strong instincts for data quality: automated checks, schema validation, and integration testing for pipeline reliability.
- Ability to evaluate new AI tools critically and choose practical solutions over hype.
Nice-to-have:
- Experience with silicon product metrics (speed, power, voltage noise, binning) and visualization/dashboarding for diverse stakeholders.
- Familiarity with MCP, DSPy, or LLM evaluation frameworks and Perl interoperability for legacy chip-data workflows.
Education Requirements
BS or MS in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, or equivalent practical experience.
About the Company
Company: NVIDIA
Headquarters: Santa Clara, California, USA
NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.
