Principal Software Engineer- ML Search Infrastructure

Snowflake•Menlo Park, CA

About The Position

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. As Snowflake’s data and metadata landscape continues to grow, providing a high-performance, scalable, and reliable search foundation is critical. The Snowscope team is responsible for the unified search experience across Snowflake—including the Catalog, Marketplace, Documentation, and Workspaces. We are looking for a Principal Software Engineer for Search Infrastructure to architect the "engine" itself. This individual will own the core search services, distributed indexing pipelines, and retrieval infrastructure that powers Universal Search. You will ensure our systems are not only robust and cost-effective but also ready to support the high-throughput, low-latency demands of next-generation AI agents and RAG (Retrieval-Augmented Generation) workflows. As a Principal Search Infrastructure engineer, you will own the search backend and indexing systems. You will make the search scale at the cost of compute while indexing will scale at the cost of storage. This role requires a leader who will drive cross-team execution through thoughtful architecture, particularly where distributed systems, data pipelines, and search technologies intersect.

Requirements

Search Infrastructure Expertise: Deep experience with the internals of search engines and vector databases.
Distributed Systems at Scale: 10+ years of experience shipping high-throughput backend platform software in production with Principal-level scope (architecture ownership and cross-team delivery).
Indexing & Data Pipelines: Proven track record building distributed data systems, including partitioning, replication, and handling complex failure modes in high-scale environments.
High Performance Computing: Expertise in optimizing systems for low-latency retrieval and high-concurrency workloads under real customer load.
Technical Visionary: Ability to translate high-level product goals into complex technical roadmaps and influence the broader engineering organization.
Container & Cloud Fundamentals: Experience with secure, cost-aware container execution (e.g., Kubernetes-class patterns) and strong security fundamentals (threat modeling, least privilege).
Proficiency in Go/Java/C++: Deep expertise in systems-level languages used for building high-performance backend infrastructure.
Hybrid Search Implementation: Practical experience implementing the infrastructure required to blend vector embeddings with traditional BM25 ranking.

Nice To Haves

Multi-tenant SaaS Experience: Background in building workflow orchestration or high-throughput async systems for multi-tenant environments.
User Experience Empathy: Understanding how infrastructure latency and reliability directly impact the end-user search experience.

Responsibilities

Infrastructure Architecture: Design and build the next generation of Snowflake’s search infrastructure, focusing on scalable indexing, storage, and low-latency retrieval for massive, heterogeneous datasets.
Vector & Hybrid Systems: Lead the architectural evolution of our hybrid search engine, ensuring seamless integration of traditional keyword-based (syntactic) and modern vector-based (semantic) search at scale.
Distributed Data Systems: Evolve data-intensive platform components including durable indexing workflows, streaming/queuing systems, and caching strategies with strong observability and capacity discipline.
AI & Agent Enablement: Architect the platform to support "agentic" search patterns, ensuring safe, secure, and performant tool-use retrieval and RAG capabilities.
Production Excellence: Define SLOs, incident patterns, and rollout practices for mission-critical search services; drive technical roadmaps that align with high-level product goals.

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