AI Applied Scientist, Code Intelligence

Snowflake•Menlo Park, CA

45d

About The Position

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. 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. We’re seeking an AI Applied Scientist to drive Code Intelligence at Snowflake. Your mission: build AI-powered capabilities that supercharge developer efficiency and elevate developer experience for both Snowflake engineers and our customer developers. You’ll turn cutting-edge ideas into dependable tools—shipping systems that make coding, reviewing, and testing faster, safer, and more delightful at scale.

Requirements

MS or PhD in Computer Science (or related field).
Strong Python skills and fluency with a commonly used ML stack (e.g., PyTorch and XGBoost or similar).
Hands-on experience building ML data pipelines, plus a habit of rigorous evaluation and experiment design.
Familiarity with serving stacks and vector stores; comfort learning what you don’t know.
Eagerness to learn build systems and CI/CD. Previous experience with build systems and CI/CD is a plus but not required.

Nice To Haves

LLMs for code, program analysis/ASTs, retrieval/RAG, prompt engineering & eval, large-scale A/B testing, backend/service development, and familiarity with OLAP systems like Snowflake.

Responsibilities

Build AI features that move the needle on developer outcomes—accelerating PR cycle time and time-to-first-commit, reducing review latency and defects, improving test coverage, and raising developer satisfaction.
Design and deliver systems in three focus areas: automated testing, code review assistance, and coding agents that integrate with repos and workflows.
Prototype quickly and iterate, then productionize and harden solutions for reliability, performance, and safety, with a strong bias toward shipping impact.
Develop end-to-end ML pipelines: data preparation, training/fine-tuning, evaluation, and online serving—including experiment design, telemetry, and A/B measurement.
Collaborate closely with Engineering Systems (our developer productivity team) to integrate into build, test, and deployment workflows.
Write clear, maintainable Python and work across the stack to design, implement, and debug components from modeling to services and evaluation harnesses.
Contribute to a culture of high-quality engineering: code health, documentation, and grounded decision-making informed by data.
Write technical blogs and research papers to share your findings.