Why This Job is Featured on The SaaS Jobs
This role sits at the intersection of modern SaaS infrastructure and applied AI, focused on agentic systems that automate developer workflows like migration and testing. In a data platform context, that emphasis is notable because it targets the practical friction points that appear when large customers adopt cloud products, where reliability and repeatability matter as much as model capability.
For a SaaS career, the work maps to a full AI feature lifecycle that many teams are still formalizing: tool and prompt engineering, evaluation design, deployment, measurement, and iterative optimization. Experience building “golden sets” and rubrics translates well across SaaS organizations adopting LLMs, since strong eval discipline is increasingly a differentiator between prototypes and production-grade product behavior. The collaboration with product and forward-deployed counterparts also reflects how enterprise SaaS teams convert field learnings into platform features.
It best suits engineers who enjoy pairing software engineering fundamentals with empirical iteration, and who prefer work that is grounded in measurable outcomes rather than research novelty alone. The scope also aligns with early-career builders looking to develop credible judgment around production AI systems in a platform environment.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.
The Cortex team is leveraging coding agents to accelerate user workflows. Our mission is to build autonomous, agentic systems that augment and automate complex coding, migration, and testing workflows.
Your work will directly impact the speed at which the world’s largest companies modernize their data stacks. You will work within a high-powered team of engineers and scientists. You'll own the full AI engineering lifecycle: design, prompt/tool engineering, evals, deployment, measurement, and optimization.
What You Will Do
Build Agentic Systems: Build sophisticated agents and tools for migrating SQL and ETL pipelines from legacy systems for customers adopting snowflake.
Quality Improvements: Create "golden sets" and rubrics to measure the accuracy of AI-driven processes across various workflows. "Hillclimb" on metrics to optimize agentic systems.
Scale Automated Coding: Scale automated coding agents to reduce human effort by 90%.
Design & Execute Evals: Collaborate with FDE and Product: Partner with Forward Deployed Engineers (FDEs) and other product teams to translate real-world challenges into automated product features, ensuring a rapid feedback loop for tooling improvements.
Requirements
Education: Bachelor’s degree in Computer Science, Engineering, or a related field.
Experience: 1-3 years of software engineering experience, with a strong interest or project experience in shipping AI/ML features or LLM-based applications.
Technical Proficiency: Strong programming skills in Python and experience with SQL (Snowflake, SQL Server, Oracle, or Teradata experience is a plus).
AI Fundamentals: Familiarity with AI engineering and agentic workflows.
Communication: Strong communication skills and ability to collaborate effectively in a team environment.
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
