AI Engineer - Database Engineering
As an AI Engineer, you will own the full AI engineering lifecycle, from design to optimization, for Snowflake Database Engineering products. You will build agentic workflows, coding harnesses, and evaluation pipelines, working with a high-powered engineering team.
About the job
About the Role
You will work on critical business initiatives in the core database engine, bring an AI-forward approach to software development and accelerate roadmap cycles for the benefit of our customers.
Your work will directly impact how developers and businesses build with data. You'll own the full AI engineering lifecycle: design, prompt/tool engineering, evals, deployment, measurement, and optimization. You'll work with a small, high-powered engineering team. What you will do in this role:
- Own features end-to-end for Snowflake Database Engineering products. Build agentic workflows, coding harnesses, evaluation pipelines.
- Build enterprise-grade context engineering: function calling, tool schemas, guardrails, agent teams, and verification/repair.
- Design evals and hillclimb : create golden sets, create rubrics and metrics, analyze errors, run experiments to hill climb on the metrics.
- Partner with product and infra: translate customer problems into products and experiments. Collaborate with infrastructure teams to productionize improvements.
- Work with an elite team of engineers towards building great products
Requirements:
- Bachelor’s degree in Computer Science, Engineering, Statistics or a related field. Master’s or higher degree preferred but not a requirement.
- 5+ years of experience shipping AI features in production.
- Proficiency in programming languages such as Python, Typescript, Go
- Strong communication skills and ability to collaborate effectively in a team environment.
- (Optional) Experience working with data engineering pipelines (dbt, airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus.
Nice to have:
- Deep experience with agentic coding tools (e.g. IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits.
- Background in data engineering (dbt, Airflow), data modeling, analytics, retrieval / RAG, or semantic layers — highly relevant for data-centric coding agents.
- Prior work on eval harnesses, LLM observability, or safety / guardrails in production.
You may be a particularly good fit if you:
- Have built and owned complex systems — pipelines, orchestration, or software with substantial state, branching logic, and operational requirements.
- Thrive in high-intensity environments with short feedback loops and high standards for rigor.
- Take problems to completion independently: you don’t stop at a prototype; you care about production reliability and clear metrics.
- Are a power user of modern coding agents and care about turning that intuition into systematic measurement and improvement.
Skills
Python, TypeScript, Go, dbt, Airflow, Data Modeling, Data Analysis, Retrieval Systems, Semantic Layers, Llm Observability
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