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Snowflake is seeking a Staff Software Engineer to build the search infrastructure powering Cortex AI, the company's flagship AI platform for enterprise data. This role focuses on designing and scaling high-performance retrieval engines that support products like CoWork, Cortex Code, and Cortex Agents, handling billions of rows of data with millisecond latency at enterprise scale.
Key responsibilities include:
- Architect agentic runtimes that orchestrate complex AI workflows with low-latency tool execution and robust state management.
- Scale context engineering infrastructure for RAG (Retrieval-Augmented Generation), including vector database integration, scalable search indexing, query processing, result ranking, semantic caching, and automated metadata extraction.
- Build an evals engine for massive-scale golden set simulations, error analysis pipelines, and hillclimbing experiments.
- Productionize AI workflows by collaborating with modeling teams to transform raw LLM capabilities into hardened, multi-tenant microservices with guardrails and observability.
- Optimize performance and cost through model routing, prompt caching, and token optimization strategies.
This is a high-impact role at the intersection of distributed systems, AI infrastructure, and enterprise data platforms. You will work on systems that power Snowflake's AI capabilities and directly influence the company's competitive position in the agentic enterprise space.
REQUIREMENTS:
- Bachelor's degree in Computer Science or related technical field
- 9+ years of experience building distributed systems, high-throughput APIs, or backend infrastructure for AI/ML products
- Deep proficiency in Go or Java (for systems) and Python (for AI orchestration)
- Strong understanding of database internals, distributed state management, and cloud-native architecture (Kubernetes, FoundationDB, etc.)
- Familiarity with AI infrastructure fundamentals: vector indices, agent platforms, and scalable data pipelines
BONUS EXPERIENCE:
- Query optimization and SQL engine internals
- Designing multi-tenant systems handling sensitive enterprise data at scale
- Developing search infrastructure for large-scale applications
- Direct experience with any of the subsystems outlined above
