6 hours ago
Base Salary
$236k - $339k/yr
Responsibilities
- Serve as technical lead for a group of 2–4 engineers and independently drive large projects from definition through production outcomes.
- Define AI model strategies, evaluations, error analyses, prompt optimizations, adaptations, and hosting or inference improvements for AI quality initiatives.
- Advance AI-aware query planning, compiler optimizations, distributed execution, scheduling, and safe result reuse for database systems initiatives.
- Improve correctness, reliability, observability, efficiency, and performance across data processing workflows.
- Co-define product capabilities, priorities, success metrics, and business strategy with product managers and leadership.
- Break projects into milestones, delegate work, unblock and mentor engineers, manage dependencies, and maintain technical quality through hands-on implementation and reviews.
Requirements
- 8+ years of relevant experience in software engineering, applied machine learning, or database systems.
- Substantial technical contributions and measurable production impact in AI quality and applied modeling or database systems; expertise in both is not required.
- Experience independently leading multi-engineer projects from definition through production.
- Strong fundamentals in algorithms, data structures, software design, debugging, and testing.
- Fluency in at least one of C++, Java, or Python.
- An empirical problem-solving approach using metrics, experiments, root-cause analysis, and tradeoffs among quality, cost, and latency.
- Strong product sense and experience connecting technical investments to customer value and business outcomes.
- AI quality depth may include modeling, evaluation methodology, statistical experimentation, data quality, error analysis, automated optimization, and optionally model fine-tuning or post-training.
- Database systems depth may include query optimizers, compilers, execution engines, distributed data processing, and performance analysis.
- Familiarity with AI infrastructure is a plus.
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.