Senior Software Engineer, Cortex Quality

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.

Cortex Code is Snowflake’s coding agent for building with data. It ships inside the platform that thousands of the world’s largest enterprises — including a large share of the Forbes Global 2000 — run their data on, which means the quality of this agent is felt by the data teams behind a meaningful slice of the global economy. We are taking coding agents from impressive demos to tools Data Science and Engineering teams depend on every day, and we hold them to a rigorous, public bar.

See our data engineering agent benchmark https://www.snowflake.com/en/blog/engineering/data-eng-bench-data-engineering-agent-benchmark/.

ABOUT THE ROLE

This is a measurement-first role that owns the quality and efficiency of Cortex Code end to end: how good the agent is, how much it costs to run, and how reliably it behaves in production. You will take agents from research capability to real, measurable user value — turning fuzzy “the agent feels worse” signals into hard metrics, running the experiments that move them, and shipping the changes that stick. You will work on a small, high-powered modeling and infrastructure team where your work reaches every developer building on Snowflake.

WHAT YOU WILL DO IN THIS ROLE

• Take agents from prototype to production: design and refine agent behaviors for real coding and data-engineering workflows, and make them reliable enough to depend on.

• Own agent quality end to end: build eval harnesses, diagnose failure modes from real agent trajectories, and run experiments that hillclimb the metrics that matter.

• Drive down cost and latency without regressing quality: prompt caching, context compaction, tool-result offloading, and cheaper model routing for sub-tasks. At Snowflake scale, efficiency is user value.