Staff Software Engineer (Cortex AI Functions)

Location: Bellevue, Washington, United States

Salary: $236K–$339.2K annually

Company

Snowflake develops a data platform that enables enterprise-scale analytics and AI-powered processing of structured and unstructured data.

What you will do

• Serve as technical lead for 2–4 engineers and drive large projects from problem definition and architecture through implementation, launch, and production outcomes.
• For AI quality initiatives, define model strategies, lead evaluation, error analysis, prompt optimization, and model adaptation while balancing accuracy, cost, and latency.
• For database systems initiatives, advance AI-aware query planning, compiler optimizations, distributed execution, scheduling, and safe result reuse in SQL and data pipelines.
• Diagnose bottlenecks and improve correctness, reliability, observability, and efficiency across data-processing workflows.
• Co-define product capabilities, priorities, and success metrics with product managers and leadership, while aligning partner teams and dependencies.
• Break projects into milestones, delegate and unblock work, mentor engineers, and contribute hands-on through design and code reviews.

Requirements

• Deep expertise in either AI quality and applied modeling or database systems, demonstrated through 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, with strong debugging and testing skills.
• Empirical problem-solving skills using clear metrics, disciplined experiments, root-cause analysis, and tradeoffs between quality, cost, and latency.
• Strong product sense and a record of co-defining product direction with product managers and leadership, connecting technical investments to customer value and business outcomes.
• AI quality depth may include modeling, evaluation methodology, statistical experimentation, data quality, error analysis, and automated optimization.
• Database systems depth may include query optimizers, compilers, execution engines, distributed data processing, and performance analysis.

Nice to have

• Hands-on experience with model fine-tuning and post-training.
• Familiarity with AI infrastructure and integrating AI into database execution and data pipelines.

Culture & Benefits

• Work in a fast-growing environment focused on challenging ordinary thinking and accelerating innovation.
• Use an experimental, AI-native approach to solving problems and delivering impact.
• Collaborate across engineering, infrastructure, product management, and leadership teams.