SENIOR SOFTWARE ENGINEER

About this role

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.

Job Location: 135 Constitution Drive, Menlo Park, CA 94025.

ELT: Engineering

REQ ID: ASHREQ-8246

Joining this team means architecting the performance, scalability, and service infrastructure that will empower the world's largest enterprises to seamlessly govern their transactional and analytical data in one place. If you are passionate about building industry-leading technologies that solve real-world scale and performance problems, this is the place to be. In this role, you will be a key contributor to the evolution of our core product: an elastic, large-scale, high-performance data processing system. We are looking for smart, enthusiastic engineers who can quickly master complex technical areas and are passionate about building new, industry-leading technologies.

AS A SENIOR SOFTWARE ENGINEER (MULTIPLE POSITIONS AVAILABLE) AT SNOWFLAKE, YOU WILL:

Design, develop, and maintain core components of AI/ML observability systems, including backend infrastructure for largescale data processing, storage, and data movement. Implement and optimize highly specialized techniques such as sketch based data aggregation and machine learning metrics to support accurate monitoring and evaluation of ML and Generative AI models. Develop and enhance AI/ML infrastructure to support reliable training, deployment, and monitoring of machine learning workloads at scale. Build and extend platform features that enable scalable ML and Generative AI use cases while improving performance, reliability, and resource efficiency. Serve as a technical lead for the design and implementation of machine learning observability, data infrastructure, and model explainability capabilities supporting customer-facing ML and Generative AI products. Telecommuting permitted.