Staff Applied Research Scientist

Snowflake•Menlo Park, CA

2d•$236,000 - $339,200

About The Position

Snowflake is seeking AI-native thinkers to join their Data Engineering organization, which builds the platform for modern lakehouse architectures. The company is investing in a new applied research line focused on verified data infrastructure and trustworthy data systems, utilizing formal methods, automated reasoning, and modern AI techniques to solve complex problems in distributed systems and developer tooling. The goal is to enhance correctness, reliability, and engineering velocity at a large scale. This role is available at both Staff and Principal levels, with the offer calibrated to the candidate's experience and impact scope.

Requirements

PhD (or equivalent research experience) in Computer Science or a closely related field.
Depth in formal methods (e.g., model checking, theorem proving, SAT/SMT, program verification, type systems, program analysis).
Depth in distributed systems (designing, reasoning about, or verifying large-scale concurrent and distributed systems).
Strong software engineering fundamentals; ability to develop production-quality code from research ideas.
Practical experience applying modern ML, including LLMs, to systems problems (code generation, synthesis, automated reasoning).
8+ years applying theoretical computer science to large-scale software systems (cloud data platforms, distributed systems, or developer infrastructure preferred).
Demonstrated ability to drive company-level initiatives in partnership with engineering and product leadership.
Track record of technical contribution to the field (publications, open-source work, patents, or comparable evidence of impact).
Comfortable in a fast-paced, ambiguous environment where impact is measured by shipped products.

Nice To Haves

Weighted more heavily for Principal-level candidates: Demonstrated ability to drive company-level initiatives in partnership with engineering and product leadership.

Responsibilities

Lead research projects applying formal methods, program analysis, automated reasoning, and AI-driven techniques to cloud data platform problems.
Translate research ideas into prototypes and then into shipped capabilities that improve quality, velocity, reliability, and operational performance.
Partner with engineering leaders, product managers, and customers to identify opportunities and deliver solutions.
Influence the engineering and product roadmap by advising on pragmatic research directions.
Train and uplevel engineering teams on new methods and scale them across the organization.
Maintain expertise through publications, conference participation, open-source contributions, and patent filings.