Used Tools & Technologies
GPU HPCRequired Skills & Competences
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.
Python @ 4
LLM @ 4
AI @ 4
Robotics @ 4
Data Pipelines @ 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 is the industry leader in high performance computing, gaming and AI. Our GPUs and SoCs give outstanding performance and efficiency, revolutionizing fields like cell research, robotics, crypto mining and more. The Silicon Co-Design Group (SCG) focuses on the silicon layer of NVIDIA's productization work: taking chips from pre-silicon estimates through to the values that ship in firmware and populate customer specs. This role centers on the simulation and configuration engines that feed firmware, manufacturing, and specification systems downstream, and on how AI will optimize and automate every step.
Responsibilities
- Help simulate power controller interplay, voltage-frequency operating points, and binning yields; build systems that push performance and power efficiency.
- Translate silicon and firmware behavior into context engineering: break down product optimization workflows into composable skills, hybrid retrieval stages, and orchestration layers.
- Integrate silicon productization tools into a custom agent harness: define tool registries (CLIs & MCPs), webhooks, trace capture, and human-in-the-loop checkpoints.
- Lead eval-driven development for applied AI in production: perform error analysis on real silicon workflows, build automated scorers of hardware reasoning, and create CI regression gates that protect product quality.
- Set the team's AI direction; mentor and grow engineers; raise the AI bar for a team with strong silicon expertise.
Requirements
- BS or MS in EE/CE/CS (or equivalent experience).
- 8+ years in silicon bringup, firmware, or productization engineering.
- Experience deploying multiple production Python services and data pipelines, including at least one LLM-backed system relied on by subject matter experts.
- Ability to read silicon characterization outputs (speed, power, voltage noise, binning) and understand the tradeoffs between them.
- Rapid hands-on familiarity with new AI tooling (able to form a working opinion from running the tooling, not just from reading documentation).
- The role may require supporting users on production paths outside typical business hours.
Compensation & Additional Information
- Base salary ranges by level:
- Level 4: 184,000 USD - 287,500 USD
- Level 5: 224,000 USD - 356,500 USD
- Eligible for equity and benefits.
- #LI-Hybrid
- Applications accepted at least until June 13, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. The company does not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.