Used Tools & Technologies
Not specified
Required 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.
Security @ 4
TypeScript @ 4
Python @ 7
CI/CD @ 4
Communication @ 4
FastAPI @ 4
React @ 4
API @ 7
Reporting @ 4
LLM @ 4
Codex @ 4
Claude Code @ 4
Observability @ 4
AI @ 4
RAG @ 4
LangChain @ 4
Prompt Engineering @ 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 SOC Design (SOCD) team is looking for an Applied AI Engineer who is passionate about eliminating bottlenecks in SOC integration workflows through intelligent automation. If you are driven to build AI-powered tools, agents, and automation solutions to dramatically reduce cycle time and manual effort, come join us.
You will be working directly with SOCD execution and methodology teams to identify areas that can be accelerated with the use of AI services, from RAG-grounded knowledge systems and LLM-powered assistants to multi-step agents that plug into the internal design infrastructure.
Responsibilities
- Develop LLM-powered tools for high-value execution tasks: design review summarization, signoff status aggregation, integration checklist enforcement, CI/CD pipeline gating, and cross-team status reporting.
- Build and deploy RAG-based knowledge systems grounded in internal design documentation and execution artifacts.
- Design AI-assisted coding workflows, including agent-based development tools, reusable prompt templates, and structured skills to accelerate engineering productivity.
- Own reliability and evaluation of AI systems, including logging, tracing, prompt regression testing, and output validation frameworks.
- Collaborate closely with SOCD execution and methodology teams to scope problems, validate solutions, and define metrics for productivity gains from deployed automation.
Requirements
- BS/MS in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
- 6+ years of experience building production-grade software systems.
- Proven experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments.
- Strong Python skills with the ability to design, prototype and productize AI-enabled services, APIs, integrations, automation workflows, and internal tools.
- Practical experience building LLM-powered agents or agentic workflows, with hands-on use of Claude Code, OpenAI Codex, Cursor, or equivalent coding agents to improve development workflows.
- Hands-on experience with LLM application frameworks (LangChain, LlamaIndex, or equivalent) and RAG architectures — including chunking, embedding models, vector databases, and retrieval design.
- Solid software engineering fundamentals and production mindset, including system design, API design, testing, CI/CD, code quality, observability, security, databases, containers, and distributed or event-driven systems.
- Ability to identify repetitive, high-friction, or knowledge-intensive workflows and turn them into practical AI-enabled tools, automations, or assistants that improve productivity and operational efficiency.
- Demonstrated end-to-end ownership of engineering solutions, from architecture and development to deployment, integration, and ongoing operations/support.
- Excellent communication skills and a collaborative, proactive approach.
Ways to stand out
- Advanced AI techniques: fine-tuning or domain-specific prompt engineering (e.g., adapting models to understand RTL patterns); experience with MCP (Model Context Protocol) or similar tool-calling standards for interoperable agent ecosystems; multi-agent orchestration frameworks.
- Knowledge of ASIC development and SOC integration to better understand user needs.
- Experience building lightweight internal tools or full-stack applications (e.g., React/TypeScript frontends with FastAPI backends) to surface AI capabilities.
Compensation and benefits
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5. You will also be eligible for equity and benefits.
Additional information
- Work model: #LI-Hybrid
- Applications for this job will be accepted at least until July 5, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.