Interested in this AI/ML Engineer role at Snowflake?
Apply Now →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.
Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.
Snowflake is growing fast and we're scaling our team to help enable and accelerate our growth. We're passionate about our people, our customers, our values and our culture! We're also looking for people with a growth mindset and the pragmatic insight to solve for today while building for the future. And as a Snowflake employee, you will be accountable for supporting and enabling diversity and belonging.
At Snowflake, the Sr. Product Marketing Manager \- AI Data Cloud is an integral member of the AI Data Cloud product marketing team responsible for driving the operational engine behind our event content. This person manages the end\-to\-end content production pipeline across our flagship events — such as Snowflake Summit, Snowflake World Tour, and Data for Breakfast — and the downstream programs that extend from them, including digital event extensions and field programs.
This highly organized and detail\-oriented candidate ensures that sessions, speakers, and assets move from concept to delivery flawlessly and on schedule. This role works closely with internal stakeholders across product marketing, strategic events, field marketing, sales, and external agencies, serving as the primary point of contact for content workstreams across both physical and digital programs.
*Location: Hybrid — Menlo Park or Dublin*
WHAT YOU'LL DO:
- Manage the end\-to\-end content production pipeline for strategic events — leveraging automation and AI\-powered tools to track sessions, speakers, and assets from submission through delivery and post\-event distribution
- Act as the primary point of contact for internal and external speakers — managing submission deadlines, review cycles, rehearsal coordination, and asset approvals across concurrent programs
- Ensure speaker portals, event content catalogs, and website content is accurate and current across all platforms
- Coordinate cross\-functional content reviews with internal stakeholders (Product Marketing, Legal, Corporate Marketing) and external agencies to ensure all assets are approved and show\-ready
- Lead the packaging and distribution of event content into reusable formats for account\-level activations, webinars, and digital follow\-ons — using AI to scale and personalize content repurposing across audiences and channels
- Partner with agency and AV production teams to ensure content assets meet technical specifications and brand requirements
OUR IDEAL CANDIDATE WILL HAVE:
- 2–5\+ years of experience in event operations, content operations, or marketing program execution in a B2B environment
- Proven ability to manage multiple concurrent workstreams and stakeholders under tight deadlines
- Experience with automating workflows with AI, project management tools, and content or event management platforms
- Strong written and verbal communication skills with a customer service orientation and solution\-focused approach
- Execution excellence with great attention to detail as well as effective written and verbal communication
- Experience in\-house at a large, highly visible technology company or event marketing agency
- Familiarity with Speaker Resource Center tooling, event CMS platforms (e.g., RainFocus, Cvent), or similar systems
- Proven success in executing multiple events and programs simultaneously using both internal and external resources
- Motivated self\-starter with a bias for action who thrives in complex, fast\-moving environments
- Data\-driven approach to evaluating the effectiveness of events and identifying opportunities for improvement
- AI\-driven mindset when implementing workflows and processes
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 listed.
- 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.
*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 $165,000 \- $216,300\.
- 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 $165K-$216K range is above the median 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $165K to $216K.
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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