This role suits an experienced engineer with a strong background in distributed systems and production-grade LLM applications who wants to lead the design of agent evaluation platforms in Menlo Park. Snowflake is hiring a Staff Software Engineer for the Frontier Security AI team located in Menlo Park, California.
Role Overview
Snowflake seeks a Staff Software Engineer for the Frontier Security AI team in Menlo Park, CA. The position focuses on developing production-grade LLM applications, intelligent agents, AI infrastructure, and evaluation systems that handle sensitive enterprise data at scale.
Key Responsibilities
Architect and build the Agentic Harness for complex, multi-step AI workflows across models, tools, data, and services.
Design stable interfaces for tool execution, context construction, state management, memory, permissions, retries, fallbacks, and human review.
Own agent quality end to end by building evaluation harnesses, representative datasets, automated graders, experiment pipelines, and release gates.
Convert ambiguous reports into measurable failure modes, reproducible tests, and durable fixes.
Analyze production agent trajectories to identify failures in reasoning, retrieval, tool use, context, orchestration, and application code.
Close the loop between production incidents, root-cause analysis, evaluation coverage, and regression prevention.
Develop offline and online measurements for task completion, correctness, groundedness, safety, latency, reliability, and cost.
Build simulation and replay infrastructure for golden-set tests, adversarial scenarios, model comparisons, and large-scale experiments.
Improve agent efficiency through model routing, prompt and semantic caching, context compaction, tool-result management, and token optimization.
Productionize new model capabilities as secure, observable, multi-tenant services with clear operational controls.
Establish standards for evaluation design, including sampling, ground-truth quality, grader calibration, leakage prevention, and statistical significance.
Define technical direction across multiple teams and lead projects whose scope extends beyond a single service.
Mentor engineers, raise the quality of architecture reviews, and remain directly involved in implementation and debugging.
Requirements
9+ years of software engineering experience, including technical leadership of complex production systems.
Direct experience shipping and operating LLM applications, AI agents, or model-backed workflows in production.
Strong background in distributed systems, service architecture, high-throughput APIs, concurrency, and failure handling.
Experience building an agent runtime, workflow engine, developer platform, evaluation system, or similar infrastructure.
Demonstrated ability to evaluate nondeterministic systems without relying on a single aggregate score.
Fluency in Python and strong proficiency in at least one systems or application language such as Java, Go, Rust, or TypeScript.
Hands-on knowledge of tool calling, structured generation, retrieval, context engineering, prompt management, and model APIs.
Experience with production observability, including structured traces, replay, metrics, logs, and incident diagnosis.
Ability to balance agent quality with latency, reliability, security, and inference cost.
Track record of setting technical direction and delivering results across organizational boundaries.
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Clear written and verbal communication with engineering, product, and leadership audiences.
Nice to Have
Building evaluation or observability infrastructure for agentic coding, data engineering, or analytics systems.
Designing human-evaluation programs, scoring rubrics, annotation workflows, or grader-calibration methods.
Working with multi-agent orchestration, long-running agents, asynchronous workflows, or durable execution.
Developing synthetic tasks, simulations, adversarial tests, red-team exercises, or safety guardrails.
Building retrieval systems that use vector search, hybrid search, semantic indexing, ranking, or caching.
Operating multi-tenant systems that process sensitive enterprise data.
Working with model training, fine-tuning, reinforcement learning, or feedback-driven optimization.
Evaluating and onboarding frontier models based on measured product outcomes.
Experience with databases, SQL engines, data platforms, Kubernetes, or cloud-native infrastructure.
Skills and Tools
Python
Java
Go
Rust
TypeScript
Kubernetes
SQL engines
Databases
Vector search and hybrid search
Salary and Benefits
Salary range: $236K – $295K
For U.S. located jobs, applicants can visit the Snowflake Careers Site for complete salary and benefits information.
About the Company
Snowflake powers the era of the agentic enterprise, looking for AI-native thinkers who treat AI as a high-trust collaborator to reinvent how work gets done and solve problems.
How to Apply
Apply on the official company careers page at careers.snowflake.com.
