Senior Software Engineer - Cortex AI - FDE

Posted 2 Days Ago
Be an Early Applicant
Menlo Park, CA, USA
Hybrid
200K-270K Annually
Senior level
Artificial Intelligence • Big Data • Cloud • Machine Learning • Software • Database • Analytics
Let's build a world where data and AI turn possibilities into reality.
The Role
Design and build backend infrastructure for agentic AI products: orchestrate low-latency agent runtimes, scale RAG/vector search systems, develop large-scale evals and testing pipelines, productionize multi-tenant AI microservices with observability and cost/performance optimizations, and collaborate with modeling teams to deliver secure, scalable enterprise AI features.
Summary Generated by Built In

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.

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

Skills Required

  • Bachelor's degree in Computer Science or related technical field
  • 7+ years building distributed systems, high-throughput APIs, or backend infrastructure for AI/ML products
  • Deep proficiency in Go or Java for systems development
  • Proficiency in Python for AI orchestration
  • Strong understanding of database internals, distributed state management, and cloud-native architecture (e.g., Kubernetes, FoundationDB)
  • Familiarity with vector indices, vector databases, RAG, and agent platforms
  • Experience in customer-facing technical roles (communicating failures and technical analysis to customers)
  • Product instinct to evaluate platform vs bespoke fixes
  • Ability to perform cross-layer debugging using logs and telemetry
  • Experience defining and using eval frameworks for LLM/agent systems to measure and improve quality
  • Comfort with ambiguity on open-ended, externally-driven problems
  • Query optimization and SQL engine internals
  • Designing multi-tenant systems that handle sensitive enterprise data at scale
  • Experience developing search infrastructure for large-scale applications
  • Direct experience with the subsystems described (agent runtimes, eval engines, RAG infra, etc.)

Snowflake Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Snowflake and has not been reviewed or approved by Snowflake.

  • Fair & Transparent Compensation Pay is considered competitive across many roles, and the company highlights structured practices aimed at pay equity. Total compensation is often described as strong at senior levels when equity and bonuses are included.
  • Equity Value & Accessibility Equity grants and an ESPP are standard, making ownership accessible and a meaningful part of total rewards. New-hire RSUs and ongoing equity alongside bonus/commission programs are emphasized.
  • Healthcare Strength Comprehensive medical coverage is offered with programs like Lyra providing up to 25 no-cost therapy/coaching sessions, alongside HSA-eligible plans. Wellness resources and additional clinical programs (such as Omada for certain conditions) broaden the scope of support.

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The Company
HQ: Bozeman, MT
9,023 Employees
Year Founded: 2012

What We Do

Snowflake powers the end-to-end data lifecycle – from ingesting and processing data to analyzing and modeling it, to building and sharing data and AI applications – helping engineers, analysts, and leaders innovate faster and achieve more with their data. We're on a mission to empower every enterprise to achieve its full potential through data and AI.

Why Work With Us

Snowflake is where data does more, and so do you. More innovating, more growing, and more collaborating. Here, you’ll find the sweet spot between building big and moving fast, in technology and your career.

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