Senior Applied AI Engineer

Snowflake4 days ago

Location

US-CA-Menlo Park

Type

Full Time

Salary

USD 200,000 – 270,000

Level

Senior

Role

Senior Applied AI Engineer

Posted

May 21, 2026

Full TimeSenior

The role

Summary

Snowflake is seeking a Senior Applied AI Engineer to lead high-impact AI customer engagements, driving enterprise AI solutions with technical expertise, strategic vision, and hands-on implementation. The role involves owning end-to-end AI program delivery, mentoring engineers, and serving as a technical advisor to strategic customers in Snowflake's mission to power the agentic enterprise.

What you'll do

Lead Customer Programs: Manage full lifecycle of complex multi-engineer AI engagements from scoping and architecture through deployment, monitoring, and handoff, ensuring delivery quality and customer outcomes.

Own AI Quality: Define quality metrics, create evaluation frameworks, develop golden datasets, and systematically improve AI system accuracy, faithfulness, and safety through continuous evaluation loops.

Grow and Mentor Engineers: Provide technical leadership and mentorship to a team of 2-6 Applied AI Engineers, including code reviews, unblocking teammates, and actively developing their skills and careers.

Deliver with Velocity: Remain a hands-on contributor designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions that translate ambiguous business objectives into robust, scalable implementations.

Productionize AI at Scale: Implement AI solutions from prototype through deployment, establishing safety guardrails, observability, and human-review workflows to maintain reliability and trustworthiness.

Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer leadership, articulating complex technical concepts to both technical and executive stakeholders.

What we look for

Technical

LLM DevelopmentProven experience building and productionizing applications using Large Language Models, including RAG and agentic workflows

AI Quality MetricsExperience defining quality metrics and evaluation frameworks for LLM and agent systems

MLOpsHands-on experience with MLOps lifecycle, including model deployment, monitoring, and evaluation in cloud environments

Education

Advanced Technical DegreeBachelor's or Master's in Computer Science, Machine Learning, AI, or related technical field preferred

Experience

Professional Experience5+ years of professional software engineering experience

Leadership ExperienceDemonstrated experience leading technical projects or teams, setting technical direction, and driving delivery

Customer-Facing RoleExperience in a customer-facing technical role

Skills

Required skills

Large Language ModelsAdvanced understanding and implementation of LLM technologies

Technical LeadershipAbility to lead technical teams and set strategic direction

CommunicationExcellent communication skills across technical and executive levels

Nice to have

Eval ToolingExperience with evaluation and observability tools like Braintrust, LangSmith, Arize

Failure Mode AnalysisExpertise in analyzing and categorizing AI system errors

Startup ExperienceBackground in high-growth, fast-paced technology environments

Compensation & benefits

Salary

USD 200,000 – 270,000 (annual)

Benefits

Competitive Compensation

Salary range of $200,000 - $270,000 with potential for additional performance bonuses

Professional Growth

Opportunity to work at the forefront of enterprise AI innovation with cutting-edge technologies

Travel Opportunities

Up to 25% travel time working directly with strategic customers

High-Impact Environment

Collaborative culture focused on innovation, impact, and rapid career development

Interview process

1
Initial Screening — Phone or video call with recruiting team to discuss background and role fit
2
Technical Interview — In-depth technical discussion focusing on AI expertise, LLM experience, and system design
3
AI System Design Challenge — Practical assessment of candidate's ability to design and evaluate complex AI systems
4
Leadership and Collaboration Interview — Assessment of mentorship capabilities, communication skills, and cross-functional collaboration
5
Final Executive Interview — Meeting with senior leadership to assess strategic thinking and cultural alignment