Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 6
Data Pipelines @ 4
Experimentation @ 6
LLM @ 4
Python @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA's Silicon Co-Design Group (SCG) brings architecture, silicon, systems, and manufacturing together to develop NVIDIA products. The team is rebuilding its silicon productization toolchain around AI.
The role focuses on simulation and configuration engines that support firmware, manufacturing, and specification systems. This includes translating chip behavior into firmware-ready contracts, building agents that explain complex chip feature interactions, and developing evaluations to prevent product-quality issues.
Responsibilities
- Simulate power-controller interplay, voltage-frequency operating points, and binning yields to improve performance and power efficiency.
- Apply silicon and firmware expertise to context engineering by decomposing silicon product-optimization workflows into composable skills, hybrid retrieval stages, and orchestration layers.
- Integrate silicon productization tools into a custom agent harness, including CLI and MCP tool registries, webhooks, trace capture, and human-in-the-loop checkpoints.
- Lead evaluation-driven development for production applied AI, including error analysis on real silicon workflows, automated hardware-reasoning scorers, and CI regression gates.
- Help define the team's AI direction and mentor and develop engineers.
Requirements
- Bachelor's or master's degree in electrical engineering, computer engineering, computer science, or equivalent experience.
- 8 or more years of experience in silicon bring-up, firmware, or productization engineering.
- Experience deploying multiple production Python services and data pipelines.
- Experience deploying at least one LLM-backed system used by subject-matter experts in their daily work.
- Ability to interpret silicon characterization outputs, including speed, power, voltage noise, or binning, and understand the tradeoffs between them.
- Ability to evaluate and use new AI tooling quickly through hands-on experimentation.
The role supports systems used directly in the product-production process, and support questions may arise outside standard business hours.
Benefits
- Equity eligibility.
- Benefits eligibility.
- NVIDIA is an equal opportunity employer committed to an inclusive work environment.
Applications will be accepted at least until August 1, 2026.
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