Compute System Arch Engineer, AI Infra - New College Grad 2027

NVIDIA is hiring a Compute System Arch Engineer, AI Infra - New College Grad 2027 in Shanghai, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, Linux and RAG among its tools and skills.
Level classes it as a junior role.
NVIDIA has 2,700+ other open roles on Level.
Posted in the last few days.

AI in this role

Compute System Architect Engineer building reusable AI infrastructure, RAG systems, and agent execution frameworks for engineering teams.

pythonlinux

ragai-agentsai-evaluationai-infrastructurellm-applicationsmulti-agent-systemsmachine-learning-engineering

We are building reusable, reliable AI infrastructure for engineering teams, transforming technical documents, source code, and engineering data into accessible knowledge and enabling AI agents to perform complex engineering tasks. In this role, you will design and develop knowledge platforms, LLM service integrations, and agent execution frameworks, taking AI capabilities from prototypes to maintainable production systems for knowledge retrieval, test development, code review, and failure analysis.

What you’ll be doing:

Design and develop reusable AI platform components and services that provide consistent interfaces for model access, knowledge retrieval, tool calling, and workflow execution.
Build data ingestion and processing pipelines for heterogeneous sources, including documents, code, and tables, supporting parsing, structured extraction, incremental updates, versioning, and provenance.
Develop retrieval-augmented generation (RAG) and knowledge infrastructure, combining vector search, keyword search, reranking, and structured knowledge to improve retrieval quality and context relevance.
Develop agentic and multi-agent workflows with tool integration, context and state management, task scheduling, failure recovery, and human review.
Build evaluation and observability systems using datasets, automated tests, regression benchmarks, and execution traces to measure and improve correctness, reliability, latency, and cost.
Own platform deployment and maintenance, including access controls, data isolation, recovery mechanisms, and release processes, supporting reliable use across multiple users and applications.
Collaborate with domain experts and application developers to translate engineering needs into platform capabilities and continuously improve them through user feedback.

What we need to see:

Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, or a related field, or equivalent experience. An MS or PhD is a plus.
Hands on experience in software engineering, infrastructure, data platforms, machine learning engineering, or related areas.
Strong Python programming skills and software engineering fundamentals, with familiarity with Linux, version control, automated testing, and CI/CD.
Hands-on development and deployment experience in at least one of the following: LLM application infrastructure, RAG systems, agent platforms, or model-serving systems.
Understanding of the core mechanisms and limitations of LLM applications, including model APIs, embeddings, context management, structured outputs, tool calling, and evaluation.
Experience with backend services, databases, and data processing, including concurrency, caching, task state management, failure recovery, and API design.
Strong debugging, testing, and analytical skills, with the ability to diagnose issues through logs, metrics, and experiments and independently validate AI-generated code and results.
Good communication, collaboration, and ownership, with the ability to learn new domains and drive solutions from requirements through delivery and maintenance.

Ways to stand out from the crowd:

Experience with enterprise knowledge bases, complex document parsing, knowledge graphs, ontology modeling, or data quality management.
Experience developing or integrating agent frameworks, multi-agent systems, MCP, or other tool protocols, with the ability to understand and extend their underlying mechanisms.
Experience with AI evaluation, security testing, permission isolation, prompt-injection defenses, or reliability engineering for nondeterministic systems.
Experience optimizing model deployment, inference services, batching, caching, or resource scheduling, with an understanding of quality, throughput, latency, and cost tradeoffs.
Demonstrated ownership of production AI systems or meaningful contributions to relevant open-source projects and C++ development experience, or a background in computer architecture, GPUs, compilers, hardware verification, or engineering developer tools.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you

How we rate this

Compute System Arch Engineer, AI Infra - New College Grad 2027 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Compute System Arch Engineer, AI Infra - New College Grad 2027 is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in China, Shanghai.
The job was posted on October 28, 2026.
Seniority: junior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsAI EvaluationAI InfrastructureLLM ApplicationsMulti Agent SystemsMachine Learning EngineeringPython

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
How do you decide that one model's output is better than another's for a given task?
Tell me about a project where ai infrastructure was part of your work. What did you do?
Tell me about a project where llm applications was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, AI agents, AI Evaluation, AI Infrastructure, and LLM Applications. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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