SOC AI Application Engineer — AI Services, Agents and Knowledge Systems

Overview

• The NVIDIA System-On-Chip(SOC) Design team is looking for a top AI Engineer with curiosity about SOC design automation, RTL integration, and chip build and assembly now.
• If you are interested in using AI to upgrade the conventional SOC Design flow, come and join us.
• We need you to be passionate about AI+Hardware.
• You are expected to help us to build AI application-layer services which would boost HW execution team's work efficiency, includes : assistants, retrieval and Q&A; workflow automation; and develop AI agent for SOC Design-related tasks.
• You will be shipping and operating AI services (APIs, orchestration, RAG, evaluation), evaluating and using modern frameworks and tools, such as LangChain (and similar stacks), RAG pipelines, and coding-agent / IDE-centric workflows (e.g.
• Claude Code-class assistants, reusable skills / playbooks for agents).
• What you’ll be doing Design, implement, and operate LLM-backed services: APIs, async jobs, streaming responses, and integration with internal tools and data sources.
• Build RAG and knowledge systems: chunking, embeddings, vector retrieval, reranking, access control, and quality/latency tuning.
• Apply agent and orchestration patterns with frameworks like LangChain (or comparable): tool use, multi-step plans, memory, and guardrails—aligned with how SOC Hardware team works.
• Improve developer and engineer experience with AI-assisted coding and repeatable “skills”: prompts, procedures, and small utilities that teams can run consistently (including patterns like Claude Code + structured skills).
• Own reliability and perform evaluation: logging, tracing, regression tests for prompts/pipelines, and metrics for usefulness and safety on proprietary data.
• Co-work with Hardware engineers from Methodology, CAD, and Design teams to scope the problem, propose the solution, implementation (in multiple iterations), and online production-ready features.
• What we need to see MS/PhD in CS, CE, EE 1–5 years of professional experience with a clear focus on AI application / AI service development (building products on top of LLMs, not only ad-hoc scripts).
• Strong Python and experience shipping services (REST/gRPC, containers, basic cloud or on-prem deployment patterns as applicable).
• Hands-on use of LLM application frameworks (e.g.
• LangChain or equivalent) and RAG (vector DBs, retrieval design, evaluation).
• Familiarity with coding agents and IDE workflows (e.g.
• Claude Code-style usage) and frameworks (skills, templates, or internal “agent packs”).
• Solid software engineering habits: dependency management, configuration, testing, and clear interfaces for other teams.
• Excellent communication and ability to work with partners who are not AI specialists.
• Ways to stand out from the crowd Hardware knowledge: RTL Coding capability; Makefile Coding capability; SOC Design know-how; Physical Design know-how; etc—enough to understand user context and data (no requirement to be a chip designer).
• Web development: lightweight UIs, internal portals, or full-stack slices (e.g.
• React/TypeScript, FastAPI + frontend) for AI features.