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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’re growing fast and looking for a Director of Brand Strategy and AI Innovation is the ultimate custodian and strategic architect of our brand’s identity, market positioning, and global footprint. Operating at the intersection of cultural storytelling and technological innovation, this role is responsible for defining the long\-term brand approach and executing high\-impact, integrated brand campaigns that drive measurable growth and awareness while caring for the operational needs of our department.
In addition to traditional brand leadership, this role leads global brand education initiatives and pioneers our brand's evolution in the age of artificial intelligence. By managing a specialized AI Brand Architect, the Director will ensure our brand voice, guidelines, and visual identity are programmatically scaled, trained, and protected across all generative AI applications and automated workflows.
We operate in a rapid\-scale environment where the landscape changes daily. You will anticipate market shifts, redefine how we connect with our audience, and pioneer new creative frontiers ensuring that as we scale, our brand remains authentic, culturally relevant, and distinctively ours.
This role reports to the VP of Brand Marketing and is part of the larger function that includes, Brand Design, Brand Content, Brand Video, Brand Voice, Customer Marketing, and Web Marketing.
WHAT YOU’LL DO
CORE BRAND STRATEGY \& POSITIONING
- Brand Roadmap: Develop and execute the multi\-year strategy to increase brand equity, awareness, and market share across diverse segments.
- Market Differentiation: Analyze market dynamics, competitor positioning, and consumer insights to help build a distinctive, defensible brand narrative that enhances product value propositions and accelerates market adoption.
- Architecture and Guidelines: Modernize and govern the brand architecture, messaging frameworks, and visual identity guidelines, ensuring they remain highly relevant yet consistent worldwide.
END\-TO\-END BRAND CAMPAIGNS
- Campaign Architecture: Partner with cross\-functional teams on the development, and launch of integrated campaigns across channels.
- Creative Excellence: Partner with the internal creative team and external creative, and media partners to deliver breakthrough storytelling.
- Measurement: Establish campaign KPIs (attribution, sentiment shifts, customer acquisition, and retention) to prove business impact.
AI GOVERNANCE AND TECHNICAL BRAND ENGINEERING
- Manage the AI Brand Architect: Direct technical talent tasked with building, fine\-tuning, creating skills and deploying AI models and agents.
- Drive AI Capability Development: Spearhead the creation of domain\-specific AI solutions and autonomous workflows that eliminate operational bottlenecks, optimize team efficiencies, and exponentially scale departmental output.
- Brand Guardrails: Translate brand guidelines into programmatic rules, training AI models to produce content that is automatically aligned with the brand's exact tone, style, and ethics.
GLOBAL BRAND EDUCATION, ADVOCACY AND OPERATIONS
- Manage the Brand Operations Manager: Direct dedicated operational talent responsible for scaling brand education globally, overseeing our Snowstore retail operations, and optimizing the end\-to\-end Brand Marketing budget and procurement processes.
- Internal Literacy: Institutionalize a deep understanding of the brand’s core ethos, strategy, and positioning across the business, regional offices, and partner agencies.
- AI Literacy Training: Design upskilling programs for the broader marketing team on how to effectively prompt and co\-create using internal, brand\-certified AI tools.
WHAT YOU’LL BRING
- 15\+ years of marketing experience, with a minimum of 5 years leading brand strategy, market positioning, and large\-scale, multi\-channel brand campaigns.
- Strong conceptual understanding of Generative AI, machine learning, and prompt engineering necessary to direct a technical AI Brand Architect.
- Champion AI tool adoption, building hands\-on proficiency, developing workflows, automations, and templates that drive repeatable efficiency at scale.
- Strong work ethic with a high degree of technical acumen, creativity, and the ability to juggle multiple inputs from stakeholders against tight timelines.
- Exceptional communication skills with the ability to pitch ideas, build relationships across the business, and both influence and listen to find common ground.
- A proven track record of advocating for across teams, developing talent, and a willingness to roll up your sleeves to dive into the work.
PREFERRED SKILLS AND EDUCATION
- Education: Bachelor’s degree in Marketing, Business, or a related field; MBA preferred.
- Software Proficiency: Highly proficient in Google Workspace apps (Slides, Docs, Sheets) and Microsoft Office Suite (Word, Excel, PowerPoint).
- Creative Tools: Familiarity with Adobe Creative Cloud and Figma. Experience with Wrike and Canva is a major plus.
LET’S DO THIS!
The Snowflake Brand Marketing team is a tight\-knit, and dynamic group of creators and makers responsible for shaping the future of the Snowflake brand. We’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge conventional thinking, and drive innovation forward while building a future for themselves and Snowflake. If you want to play a part in creating this global iconic brand, we want to hear from you!
*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 $218,000 \- $285,600\.
- 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 $218K-$285K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Snowflake, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($251K) sits 15% above the category median. Disclosed range: $218K to $285K.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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