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About This Role
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI\-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high\-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low\-ego individuals who thrive in dynamic and fast\-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
WHO WE ARE
At Snowflake, we are powering the era of the agentic enterprise. Snowflake delivers the AI Data Cloud — a global network where thousands of organizations mobilize data with near\-unlimited scale, concurrency, and performance. Inside the AI Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflake’s platform is the engine that powers and provides access to the AI Data Cloud, creating a platform for Data Engineering, Analytics, AI and Apps and Collaboration. Join Snowflake customers, partners, and data providers already taking their businesses to new frontiers in the AI Data Cloud. snowflake.com
WHO YOU ARE
We are seeking a senior Brand AI Architect to help formalize and scale how AI powers brand management and execution in the world of AI. The ideal candidate is a technically deep builder with a strong foundation in design and brand systems — someone who can ship AI\-powered tools and automation one day, and run an enablement session for the marketing team the next.
You'll work across multiple brand teams and surfaces acting as both the architect who builds the systems and the partner who makes sure they're adopted, governed, and sustained.
This role sits at the intersection of engineering, brand execution, and systems thinking. Join our creative and enthusiastic team, where your work will directly shape how one of the most visible brands in technology leverages AI at scale. This role will report into the Director of Brand Strategy and AI Innovation.
WHAT YOU’LL DO
Build and Ship AI\-Powered Tools and Workflows
- Build and own a growing library of internal AI tools — custom web apps, Streamlit interfaces, prompt\-powered utilities, and automated workflows — that brand teams use in daily production
- Design and implement automation pipelines that connect creative tools, content systems, and AI APIs, reducing manual overhead and building in the consistency that brand work requires at scale
- Integrate AI capabilities directly into creative production workflows, including generative asset creation tools, design application features, and content workflows using LLMs
- Own the full lifecycle of everything you ship — from initial scope and build through deployment, iteration, and ongoing maintenance
Enable Adoption Across Brand Teams
- Serve as the shared AI implementation resource across brand marketing functions and surfaces — this is a core pillar of the role, not a side responsibility
- Educate and enable internal teams on the AI tools and systems you're building — running onboarding sessions, writing accessible documentation, and being the go\-to resource for practical AI adoption
- Build enablement resources — playbooks, templates, prompt libraries, training — so brand teams can confidently use AI tools without breaking brand standards
- Partner directly with each team to understand their specific needs, then build solutions that are reusable across the broader brand org
Define and Maintain Governance Standards
- Define and evolve the standards that make AI tooling sustainable: prompt libraries, model selection criteria, output evaluation frameworks, and usage guardrails that protect brand integrity
- Be the first line of defense on brand consistency in AI\-generated outputs across all touchpoints
- Surface insights to leadership: what's working, what's not, where the next gaps are
Stay Ahead of the Landscape
- Evaluate new AI platforms, models, and tools — rapidly prototyping with the ones that have genuine application for brand and creative work
- Bring a strong, informed point of view on where AI adds real value versus where it introduces risk or noise
WHAT YOU’LL BRING
- 8–10 years of combined experience in systems thinking, creative technology, software development, AI engineering, or an adjacent field — with meaningful time spent building tools for creative, brand, or marketing teams
- 3–5 years of hands\-on AI experience, including production\-proven work with LLM APIs (OpenAI, Anthropic, Google, etc.) and advanced prompt engineering. You've built workflows and tools powered by these, not just explored them
- Demonstrated experience building automation pipelines using tools or custom scripting that connect APIs, creative platforms, and data systems in real working environments
- Solid design foundations — understanding of brand systems, the intent behind creative decisions, and the tools that support brand capabilities
- A systems thinker's instinct: you design for the workflow, not just the task. You understand upstream and downstream effects, and you build solutions people can maintain
- Strong communication skills across creative and technical audiences. You can explain model tradeoffs to a designer and articulate brand constraints to a developer
- The ability to operate autonomously: scope ambiguous problems, make pragmatic calls, and prioritize across multiple teams and workstreams
- Knowledgeable and comfortable working within the Snowflake ecosystem and tools
PREFERRED SKILLS AND EDUCATION
- Bachelor's degree or equivalent experience in Design, Computer Science, Human\-Computer Interaction, or a related field
- Record of building tools that teams adopt and sustain — not just prototypes, but things that stick
- Experience working within or directly alongside creative organizations (agency or in\-house brand team) with firsthand understanding of how brand and creative production operates
- Familiarity with graphic design, web, motion graphics, or video production workflows
- Experience implementing AI governance, responsible AI frameworks, or brand compliance systems
LET’S DO THIS!
The Snowflake Brand Marketing team is a tight\-knit, dynamic group responsible for shaping the future of the Snowflake brand — and we're rewriting the playbook on how brand teams operate in an AI\-first world. We're scaling our team to help enable and accelerate our growth, and we're looking for builders who see AI not as a buzzword but as a fundamental shift in how great brands get built. If you want to work on a team that ships AI\-powered tools alongside world\-class creative, and you want to play a part in creating a global iconic brand, we want to hear from you. How will you make an impact?
*Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.*
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
The following represents the expected range of compensation for this role:
- The estimated base salary range for this role is $148,000 \- $194,200\.
- Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan.
The successful candidate’s starting salary will be determined based on permissible, non\-discriminatory factors such as skills, experience, and geographic location. This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending \& health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits.
To comply with pay transparency requirements and other statutes, you can notify us if you believe that a job posting is not compliant by completing this form.
Salary Context
This $148K-$194K range is below the median for AI Architect roles in our dataset (median: $197K across 33 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Snowflake, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $254,798 based on 67 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($171K) sits 33% below the category median. Disclosed range: $148K to $194K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Snowflake AI Hiring
Snowflake has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Menlo Park, CA, US. Compensation range: $97K - $285K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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