Staff Software Engineer, Cortex AI Functions

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. About the team Our vision is to make raw multimodal data agent-ready and useful for analytics at enterprise scale. With Snowflake AI Functions, we enable pipelines that transform PDFs, images, audio, and video into high-quality information for downstream agents and applications, with the scalability and performance of a data platform. We bring semantic operators into SQL—extraction, classification, filtering, joins, summarization, and aggregation—so customers can process and analyze structured and unstructured data together in a single hybrid query or pipeline through Snowflake’s SQL query surface. Our work connects AI quality with database systems through AI-aware query planning, adaptive model selection, and semantic query optimization. We seek deep expertise in either AI quality and applied modeling or database systems. As a Staff Software Engineer you will - Serve as tech lead for a group of 2–4 engineers, independently driving a large project end to end—from problem definition and architecture through implementation, launch, and production outcomes. - For AI quality initiatives, define model strategies tailored to product needs, balancing accuracy, cost, and latency. Drive evaluation, error analysis, prompt optimization, and model adaptation, including fine-tuning and post-training where appropriate. Guide model-specific hosting and inference optimizations with infrastructure partners, validating their impact on quality and performance. - For database systems initiatives, advance AI-aware query planning, compiler optimizations, distributed execution, scheduling, and safe result reuse within SQL and data pipelines. - Connect quality objectives with execution decisions; diagnose bottlenecks and improve correctness, reliability, observability, and efficiency across the data processing workflow. - Co-define the product surface and business strategy with PMs and leadership, translating customer needs into product capabilities, priorities, and success metrics. Align partner teams and manage dependencies through delivery. - Break complex projects into milestones, delegate work, unblock and mentor engineers, and maintain technical quality through hands-on contributions and design and code reviews. Our ideal candidate will have - Deep expertise in either AI quality and applied modeling or database systems, supported by substantial technical contributions and measurable production impact. Expertise in both is not required. - 8+ years of relevant experience in software engineering, applied machine learning, or database systems, including independently leading multi-engineer projects from definition through production. - Strong fundamentals in algorithms, data structures, and software design; fluency in at least one of C++, Java, or Python; and strong debugging and testing skills. - An empirical approach to problem solving: clear metrics, disciplined experiments, root-cause analysis, and sound tradeoffs between quality, cost, and latency. - Strong product sense and a track record of co-defining product direction with PMs and leadership, connecting technical investments to customer value and business outcomes. Relevant depth in either area AI quality: modeling, evaluation methodology, statistical experimentation, data quality, error analysis, and automated optimization. Prior hands-on experience with model fine-tuning and post-training is a plus for AI quality candidates, but is not required. Database systems: query optimizers, compilers, execution engines, distributed data processing, and performance analysis. Familiarity with AI infrastructure is a plus; our systems focus is integrating AI into database execution and data pipelines. 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 http://careers.snowflake.com