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# Staff Software Engineer, Cortex AI Search Infrastructure

- **Company:** SNOWFLAKES ABA, LLC
- **Location:** Menlo Park, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Databases, Distributed Systems, Python (Programming Language), Query Optimization, SQL Databases, Enterprise Data Management, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Backend, Kubernetes, Information Technology, Data Pipelines, Automation Anywhere, Microservices
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/staff-software-engineer-cortex-ai-search-infrastructure-snowflake-com-8303767

## 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., * 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.


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