5 months ago
Base Salary
$236k - $339k/yr
Responsibilities
- Architect and scale agentic runtimes that execute complex workflows with low-latency tool execution and robust state management.
- Design high-performance RAG infrastructure covering vector database integration, search indexing, query processing, result ranking, semantic caching, and metadata extraction.
- Develop an evaluation engine for large-scale golden-set simulations, error analysis pipelines, and hillclimbing experiments.
- Productionize LLM capabilities as hardened, observable, secure, and multi-tenant microservices with guardrails.
- Direct infrastructure strategy for model routing, prompt caching, and token optimization.
Requirements
- Bachelor’s degree in Computer Science or a related technical field.
- 5+ years of experience building distributed systems, high-throughput APIs, or backend infrastructure for AI/ML products.
- Deep proficiency in Go or Java for systems development and Python for AI orchestration.
- Strong understanding of database internals, distributed state management, and cloud-native architecture, including Kubernetes and FoundationDB.
- Familiarity with vector indices, agent platforms, and scalable data pipelines.
- Bonus experience with query optimization, SQL engine internals, sensitive-data multi-tenant systems, large-scale search infrastructure, or the listed subsystems.
Tech Stack
Categories
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.