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.

Responsibilities

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.

Requirements

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.
• 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.

Nice to Have

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.