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# Staff Software Engineer, Cortex AI Search Infrastructure

- **Company:** Snowflake Inc.
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Salary:** $339,250.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, ARM Architecture, Cloud Engineering, Databases, Distributed Systems, Python (Programming Language), Query Optimization, Search Technologies, SQL Databases, Enterprise Data Management, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Backend, Kubernetes, Information Technology, Data Pipelines, Automation Anywhere, Microservices
- **Published:** October 26, 2026
- **Apply:** https://www.adzuna.com/details/5713554642

## About the Role

* Education: Bachelor's degree in Computer Science or a related technical field.
 * Experience: 9 + years of experience building distributed systems, high-throughput APIs, or backend infrastructure for AI/ML products.
 * Technical Stack: Deep proficiency in Go or Java (for systems) and Python (for AI orchestration).
 * Systems Thinking: Strong understanding of database internals, distributed state management, and cloud-native architecture (Kubernetes, FoundationDB, etc.).
 * Domain Expertise: Familiarity with the "plumbing" of AI: vector indices, agent platforms, and building scalable data pipelines.

(Bonus) Experience with:

 * Query optimization and SQL engine internals.
 * Designing multi-tenant systems that handle sensitive enterprise data at scale.
 * Developing search infrastructure for large-scale applications.
 * Direct experience with any of the subsystems outlined above.

## Description

* Architect Agentic Runtimes: Build and scale the orchestration engines that execute complex agentic workflows, ensuring low-latency tool execution and robust state management.
 * Scale Context Engineering Infra: Design high-performance systems for RAG (Retrieval-Augmented Generation), including vector database integration, scalable and efficient search indexing, query processing, and result ranking, semantic caching, and automated metadata extraction.
 * Build the "Evals Engine": Develop the automated infrastructure required to run massive-scale golden set simulations, error analysis pipelines, and "hillclimbing" experiments.
 * Productionize AI Workflows: Collaborate with the modeling team to take raw LLM capabilities and turn them into hardened, multi-tenant microservices with strict guardrails and observability.
 * Optimize Performance & Cost: Direct the infra strategy for model routing, prompt caching, and token optimization to ensure Snowflake's AI features are the most efficient in the industry.