Senior SOCD Applied AI Engineer

NVIDIA•Santa Clara, CA

5d•$168,000 - $310,500•Hybrid

About The Position

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. This role involves building AI-powered tools, agents, and automation solutions to dramatically reduce cycle time and manual effort. The engineer will work directly with SOCD execution and methodology teams to identify areas that can be accelerated with AI services, including RAG-grounded knowledge systems, LLM-powered assistants, and multi-step agents integrated into the internal design infrastructure.

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.

Nice To Haves

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.

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.