Senior ML Architect, Applied Field Engineering

Snowflake5 months ago

Location

US-NC-Remote

Workplace

Remote

Type

Full Time

Salary

USD 165,000 – 216,562

Level

Senior

Role

ML Engineer

Posted

Sep 18, 2025

Full TimeRemoteSenior

The role

Summary

Snowflake is seeking a Senior ML Architect for their Applied Field Engineering team to serve as a customer-facing AI/ML Solutions Architect, providing strategic technical advisory and hands-on expertise to enterprise clients. This role requires 5+ years of experience building and deploying machine learning solutions in the cloud, with strong MLOps experience and presentation skills to work with both technical and executive stakeholders.

What you'll do

Technical AI/ML Leadership: Serve as the technical expert positioning Snowflake's AI and ML features to technical stakeholders across the Americas

Customer POC Management: Partner with account teams and customer champions to scope, drive, and deliver successful proof-of-concepts that demonstrate Snowflake's value

Demo Development: Build compelling AI/ML demonstrations and proof-of-concepts for prospective customers

Product Roadmap Influence: Collaborate with Snowflake's product and engineering teams to influence AI and ML roadmaps based on customer feedback

Technical Content Creation: Publish scalable content including blog posts, conference presentations, technical notebooks, and demos

Sales Engineering Support: Influence, tailor, and maintain AI/ML selling assets including customer presentations, demonstrations, and success stories

Executive Reporting: Deliver executive readouts and business value cases demonstrating technical wins and ROI

What we look for

Technical

ML/AI Development Experience5+ years of experience building and deploying machine learning and generative AI solutions in cloud environments

MLOps Platform ExpertiseExperience with major ML platforms (Databricks, SageMaker) including pipeline development, production model deployment, monitoring, and testing

Python ProgrammingHands-on scripting experience with Python and ML libraries (Pandas, HuggingFace, XGBoost, PyTorch, TensorFlow, SciKit-Learn)

Data Engineering SkillsProficiency in data cleansing, working with large datasets, data quality evaluation and checks

Feature EngineeringAbility to determine relevant features for model training and evaluation

Model OptimizationExperience in optimizing model performance and accuracy

MLOps Lifecycle ManagementKnowledge of machine learning operations and complete model lifecycle management

Education

Bachelor's DegreeRequired in computer science, engineering, mathematics, or related fields

Master's DegreePreferred in computer science, engineering, mathematics, or related fields, or equivalent experience

Experience

Presentation SkillsStrong skills presenting to both technical and executive audiences, including impromptu whiteboard sessions and formal presentations

LLM EcosystemBonus: Working knowledge of tools like LangChain, LlamaIndex, and NeMo-Guard

Cloud InfrastructureBonus: Understanding of large-scale IaaS platforms (AWS, Azure, GCP)

Snowflake ExperienceBonus: 1+ years of practical Snowflake experience

Skills

Required skills

Machine Learning5+ years building and deploying ML and generative AI solutions

Python ProgrammingHands-on scripting with ML libraries and frameworks

MLOpsProduction model deployment, monitoring, and lifecycle management

Data EngineeringData cleansing, quality evaluation, and feature engineering

Technical PresentationAbility to present to technical and executive audiences

Nice to have

LLM ToolsExperience with LangChain, LlamaIndex, and NeMo-Guard

Cloud PlatformsAWS, Azure, or GCP infrastructure knowledge

Snowflake1+ years of practical Snowflake platform experience

Advanced EducationMaster's degree in relevant technical field

Compensation & benefits

Salary

USD 165,000 – 216,562 (annual)

Benefits

Competitive Salary

Salary range of $165,000 - $216,562 based on experience and qualifications

Remote Work

Fully remote position with flexibility to work from anywhere in the US

Professional Development

Opportunities to publish content, speak at conferences, and influence product roadmaps

Innovation Culture

Culture focused on impact, innovation, and collaboration with cutting-edge technology

Interview process

1
Application Review — Initial screening of resume and application materials focusing on ML experience and technical background
2
Phone/Video Screen — 30-45 minute conversation with hiring manager covering background, motivation, and basic technical concepts
3
Technical Interview — Deep dive into ML/AI experience, MLOps knowledge, and hands-on coding with Python and ML libraries
4
Presentation Round — Technical presentation demonstrating ability to explain complex AI/ML concepts to different audiences
5
Customer Scenario — Role-playing exercise simulating customer interaction and technical advisory scenarios
6
Final Interview — Leadership and cultural fit assessment with senior team members