The Cortex Apps team is building the future of AI for enterprise data. This role focuses on the
backend infrastructure that powers our flagship products like
Snowflake Intelligence,
Cortex Agents and Search making agentic AI fast, reliable, scalable and secure at the enterprise level.
You won't just be using AI tools; you will be building the high-performance systems that orchestrate them. You'll own and influence the architecture for agent execution environments, high-throughput context retrieval, or the ecosystem that allows our customers to iterate and launch agents in production.
What you will do in this role:- 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.
Requirements:- Education: Bachelor's degree in Computer Science or a related technical field.
- Experience: 7+ 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.
- Experience in a customer-facing technical role - you have explained a hard failure to a frustrated external audience and been believed, you can produce both the internal analysis and the customer-safe version.
- Product instinct - you can judge whether one customer's problem is bespoke or a platform gap worth fixing for everyone. This is the core judgment call.
- Cross-layer debugging - tracing a request across services and root-causing in unfamiliar code from logs and telemetry, not guesswork.
- Eval frameworks for LLM/agent systems - defining quality metrics and using evals to improve quality systematically over time.
- Comfort with ambiguity on open-ended, externally-driven problems.
(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.
Snowflake is growing fast, and we're scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
