Staff AI Engineer - Snowflake Intelligence

Snowflake11 months ago

Location

US-CA-Menlo Park

Type

Full Time

Salary

USD 200,000 – 287,500

Level

Staff

Role

Staff AI Engineer

Posted

Mar 25, 2025

Full TimeStaff

The role

Summary

Snowflake's Cortex Apps team seeks a Staff AI Engineer to build enterprise-grade AI applications for the AI Data Cloud platform. This role involves owning the full AI engineering lifecycle from design to deployment, working on flagship products like Snowflake Intelligence, Cortex Agents, and natural language-to-SQL systems that help businesses interact with their data through conversational AI interfaces.

What you'll do

End-to-End Feature Ownership: Own complete development lifecycle for Snowflake Cortex products including agentic workflows, NL-to-SQL systems, and semantic search capabilities

Enterprise Context Engineering: Build production-grade systems for function calling, tool schemas, guardrails, and semantic model-aware SQL prompting with verification and repair mechanisms

AI Evaluation and Optimization: Design comprehensive evaluation frameworks, create golden datasets, establish metrics and rubrics, analyze model errors, and run experiments to improve system performance

Cross-Functional Partnership: Collaborate with product managers to translate customer requirements into technical solutions and work with infrastructure teams to productionize AI improvements

Technical Leadership: Lead and mentor a team of engineers in building scalable AI products that serve enterprise customers at scale

Production AI Deployment: Deploy and maintain AI systems in production environments with focus on reliability, performance, and enterprise-grade security requirements

Model Performance Monitoring: Implement comprehensive monitoring and alerting systems to track AI model performance, accuracy, and system health in production

Research and Innovation: Stay current with latest AI/ML research and evaluate new technologies for potential integration into Snowflake's AI platform

What we look for

Technical

AI Production Experience5+ years of hands-on experience shipping AI features and models to production environments

Programming ProficiencyStrong expertise in Python and Go for building scalable AI applications and backend systems

Machine Learning EngineeringDeep understanding of ML model lifecycle, deployment strategies, and production monitoring

Natural Language ProcessingExperience with NLP, text processing, and conversational AI systems

Distributed SystemsKnowledge of building and scaling distributed systems for high-availability AI applications

Education

Bachelor's DegreeBachelor's degree in Computer Science, Engineering, Statistics, or related technical field

Advanced Degree PreferredMaster's or PhD in relevant field is preferred but not required

Experience

AI/ML Production Experience5+ years of experience building and deploying AI/ML systems in production environments

Enterprise Software DevelopmentExperience building enterprise-grade software with focus on reliability, scalability, and security

Team LeadershipExperience leading technical teams and mentoring engineers in AI/ML development

Text-to-SQL SystemsOptional but valuable experience with natural language to SQL translation systems

Data Analytics PlatformsExperience with data modeling, analysis, and semantic layer technologies

Skills

Required skills

Python ProgrammingAdvanced proficiency in Python for AI/ML development, data processing, and backend services

Go ProgrammingStrong Go skills for building high-performance distributed systems and microservices

Machine LearningDeep expertise in ML algorithms, model training, evaluation, and production deployment

AI System DesignAbility to architect and design scalable AI systems for enterprise applications

Production EngineeringExperience with deploying, monitoring, and maintaining AI systems in production environments

Communication SkillsStrong verbal and written communication for cross-functional collaboration and technical leadership

Nice to have

Natural Language ProcessingExperience with NLP techniques, text processing, and conversational AI development

Text-to-SQL SystemsKnowledge of natural language to SQL translation and semantic parsing

Data ModelingExperience with data modeling, semantic layers, and enterprise data architecture

Retrieval SystemsUnderstanding of information retrieval, search systems, and RAG architectures

Large Language ModelsExperience working with LLMs, prompt engineering, and fine-tuning techniques

Cloud PlatformsFamiliarity with AWS, Azure, or GCP for cloud-native AI application development

KubernetesContainer orchestration experience for scalable AI service deployment

Compensation & benefits

Salary

USD 200,000 – 287,500 (annual)

Stock options

Available

Benefits

Comprehensive Health Insurance

Medical, dental, and vision coverage for employees and families

Equity Compensation

Stock options and equity participation in Snowflake's growth as a publicly traded company

Flexible Time Off

Unlimited PTO policy allowing for work-life balance and personal time management

Professional Development

Learning and development budget for conferences, courses, and skill advancement

Remote Work Support

Home office stipend and flexible hybrid work arrangements

Retirement Benefits

401(k) plan with company matching contributions

Parental Leave

Comprehensive paid parental leave for new parents

Wellness Programs

Mental health support, fitness reimbursements, and employee assistance programs

Interview process

1
Initial Screening — 30-minute phone/video call with recruiter to discuss background, experience, and role expectations
2
Technical Phone Screen — 45-minute technical interview focusing on AI/ML concepts, system design, and coding in Python
3
AI Engineering Deep Dive — 60-minute interview covering ML model lifecycle, production deployment strategies, and AI system architecture
4
System Design Interview — 90-minute session designing a large-scale AI system similar to Snowflake's Cortex products
5
Behavioral and Leadership — 45-minute interview assessing leadership experience, team collaboration, and cultural fit
6
Final Round with Senior Leadership — 30-minute discussion with engineering leadership about vision, career goals, and strategic thinking
7
Reference and Background Check — Verification of employment history, education, and professional references