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

• 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.

• 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.

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