Job description

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. Snowflake's Data Engineering organization builds the platform that ingests, transforms, and stores data for modern lakehouse architectures — powering billions of queries, DML, and DDL operations with industry-leading price-performance. We lead the industry's shift to open data lakes through our work on Iceberg and Polaris, and we deliver capabilities like Snowpark, Dynamic Tables, cross-region replication, time travel, and zero-copy cloning at enterprise scale. We are investing in a new line of applied research — building toward verified data infrastructure and trustworthy data systems — that brings formal methods, automated reasoning, and modern AI techniques to bear on the hardest problems in our distributed systems and developer tooling. The goal is to improve correctness, reliability, and engineering velocity at a scale very few platforms operate at. We're hiring at both the Staff and Principal level; we'll calibrate the offer to the candidate's experience and scope of impact. WHAT YOU'LL DO - Lead research projects that apply formal methods, program analysis, automated reasoning, and AI-driven techniques (including code generation and modeling) to real problems in our cloud data platform. - Translate research ideas into prototypes, then into shipped capabilities that move concrete business metrics — quality, velocity, reliability, and operational performance at scale. - Partner closely with engineering leaders, product managers, and key customers to identify high-leverage opportunities and turn them into deliverables. - Influence the engineering and product roadmap; advise leaders on which research directions are pragmatic and which are not. - Train and uplevel engineering teams on new methods, and scale those methods across the organization. - Maintain expertise at the frontier of the field through publications, conference participation, open-source contributions, and patent filings. WHAT WE'RE LOOKING FOR - PhD (or equivalent research experience) in Computer Science or a closely related field. - Depth across the areas this role sits at the intersection of: - Formal methods — e.g., model checking, theorem proving, SAT/SMT, program verification, type systems, or program analysis. - Distributed systems — designing, reasoning about, or verifying large-scale concurrent and distributed systems. - Software engineering — strong fundamentals; able to go from a research idea to production-quality code in collaboration with engineering teams. - AI / ML — practical experience applying modern ML, including LLMs, to systems problems such as code generation, synthesis, or automated reasoning. - 8+ years applying theoretical computer science to large-scale software systems — ideally cloud data platforms, distributed systems, or developer infrastructure. - Demonstrated ability to drive company-level initiatives in partnership with engineering and product leadership. (Weighted more heavily for Principal-level candidates.) - 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 what ships. ABOUT WORKING HERE Every Snowflake employee is expected to follow the company's confidentiality and security standards for handling sensitive data, and to keep customer information secure and confidential as an essential part of their duties. 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 careers.snowflake.com

Requirements

Must-haves

Lead research projects that apply formal methods, program analysis, automated reasoning, and AI-driven techniques (including code generation and modeling) to real problems in our cloud data platform.
Translate research ideas into prototypes, then into shipped capabilities that move concrete business metrics — quality, velocity, reliability, and operational performance at scale.
Partner closely with engineering leaders, product managers, and key customers to identify high-leverage opportunities and turn them into deliverables.
Influence the engineering and product roadmap; advise leaders on which research directions are pragmatic and which are not.
Train and uplevel engineering teams on new methods, and scale those methods across the organization.
Maintain expertise at the frontier of the field through publications, conference participation, open-source contributions, and patent filings.
PhD (or equivalent research experience) in Computer Science or a closely related field.
Depth across the areas this role sits at the intersection of:
Formal methods — e.g., model checking, theorem proving, SAT/SMT, program verification, type systems, or program analysis.
Distributed systems — designing, reasoning about, or verifying large-scale concurrent and distributed systems.
Software engineering — strong fundamentals; able to go from a research idea to production-quality code in collaboration with engineering teams.
AI / ML — practical experience applying modern ML, including LLMs, to systems problems such as code generation, synthesis, or automated reasoning.
8+ years applying theoretical computer science to large-scale software systems — ideally cloud data platforms, distributed systems, or developer infrastructure.
Demonstrated ability to drive company-level initiatives in partnership with engineering and product leadership. (Weighted more heavily for Principal-level candidates.)
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 what ships.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigoml

Platform: ashby
Effort: quick