Snowflake is actively hiring for 14 AI and machine learning positions across AI/ML Engineer (7), AI Product Manager (4), and AI Software Engineer (2) roles. Posted salary ranges span $126K - $339K, though only 28% of listings disclose compensation. The median posted ceiling sits at $177K. Positions are based in Bellevue, WA, US, Denver, CO, US, Menlo Park, CA. The most frequently requested skills across these postings are Python, Aws, Azure, Gcp, Hugging Face. Senior-level roles account for 57% of openings.
Skills & Technologies
Locations
Bellevue, WA, US, Denver, CO, US, Menlo Park, CA, Dublin, CA, US, Menlo Park, CA, US
Hiring by Role Category
Open Positions (14)
AI Software Engineer
AI Associate, Office of the CEO
Senior Product Manager - AI Migrations
AI Solutions Specialist
Senior AI Engineer, Enterprise
Software Engineer - SnowConvert AI
AI Product Sales Specialist
Technical Product Marketing Lead, AI Data Cloud
Principal Solutions Architect - AI/ML, Services Delivery
Director of Brand Strategy and AI Innovation
GTM Staff Data Scientist
Senior Product Manager - Enterprise AI
Senior Product Manager - Applied AI
Staff Product Manager - AI FDE
What Snowflake's hiring tells you
14 open AI roles across 4 role types puts this company in the scaling phase: past the initial proof of concept, building out a real team. Expect more structure than a startup but less bureaucracy than a major. Good fit for engineers who want ownership without building from zero. Posted compensation range ($126K - $339K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in AI Software Engineer roles. That is a clue about what Snowflake is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the Snowflake interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- What problem did the first AI hire solve, and how has scope grown since?
- Where does AI sit in the engineering org, and who owns the budget?
- What is the on-call expectation for AI systems? (If unclear, that means it has not happened yet.)
Snowflake AI and ML Hiring
Snowflake has 14 active AI and ML roles in our dataset. Open positions span AI Software Engineer, AI/ML Engineer, AI Product Manager, Data Scientist. Compensation ranges from $126K - $339K across disclosed roles. Roles are based in Bellevue, WA, US, Denver, CO, US, Menlo Park, CA, Dublin, CA, US.
Salary Benchmarks
The market median for AI roles is $213,480. AI Software Engineer roles pay a median of $220,000 across the market. AI/ML Engineer roles pay a median of $212,160 across the market. AI Product Manager roles pay a median of $220,350 across the market. Top-quartile AI compensation starts at $264,111.
Skills Snowflake Looks For
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
AI Role Categories
AI Software Engineer
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Market compensation for AI Software Engineer roles: $220,000 median across 1,689 positions with disclosed pay.
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $212,160 median across 13,809 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $220,350 median across 994 positions with disclosed pay.
Data Scientist
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Market compensation for Data Scientist roles: $190,000 median across 1,566 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Frequently Asked Questions
Frequently Asked Questions
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