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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.
We are an AI\-first analytics team. We don't use AI to augment traditional BI workflows — we've replaced them. The Finance Analytics team builds the intelligence layer that Strategic Finance runs on: AI agents that encode repeatable finance processes, Streamlit apps that surface real\-time insight, semantic models that let any analyst query complex data in plain English, and workflow automations that collapse hours of manual work into a single prompt.
Our primary development environment is CoCo (Cortex Code), Snowflake's AI coding assistant, and SnowWork, the AI IDE we ship work in. Every deliverable on this team is built AI\-first: you design the workflow, you write the prompt, you validate the output. If you are still building dashboards by hand, refreshing Excel files manually, or treating AI as a spell\-checker for your code — this role will ask you to operate differently.
This is a high\-breadth seat. One week you're building a new AI agent for quarterly revenue analysis; the next you're designing a sensitivity analysis tool for an earnings war room. You are equally comfortable in an AI\-IDE, a Python file, and a stakeholder summary for a senior finance leader.
WHAT YOU'LL WORK ON
AI AGENT AND WORKFLOW DEVELOPMENT
*(PRIMARY FOCUS)*
- Design and build skills and agentic experiences that encode repeatable finance workflows — revenue analysis, cost monitoring, earnings prep, headcount tracking — into reusable, invokable tools using CoCo and SnowWork
- Write and iterate on prompt \& skill structures (YAML \+ Markdown skill files) based on output quality and stakeholder feedback
- Build skills that allows non\-technical finance analysts to produce analyst\-quality output in a single prompt
- Evaluate model outputs rigorously — you are the quality gate before anything reaches a finance stakeholder
FINANCE ANALYTICS
- Build and maintain quarterly and weekly revenue summary pipelines
- Support sensitivity analysis models for quarterly business reviews \& revenue forecast scenarios
- Produce ad\-hoc analysis for Strategic Finance
SEMANTIC LAYER \& APPLICATION DEVELOPMENT
- Build and improve semantic data models that expose finance tables to natural language queries via Cortex Analyst
- Develop and deploy production finance dashboards as Streamlit apps (locally and deployed to Snowflake)
- Build customer\-facing demo applications for Sales and Field teams
- Apply reusable component patterns and shared utility libraries for consistent, polished UI
EARNINGS AND REPORTING AUTOMATION
- Participate in quarterly earnings cycle prep — scenario tooling, export automation, IR data requests
- Build and maintain source\-of\-truth reporting exports (multi\-tab Excel, formatted to spec)
- Support ad\-hoc disclosure and investor relations data needs during quarter\-end
HARD SKILLS REQUIRED
MUST\-HAVE
AI\-assisted development — You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your *primary* development tool — not an occasional helper, not a code reviewer. You know how to write a prompt that produces production\-ready output, how to steer a model that's heading in the wrong direction, and how to encode domain logic into a reusable, parameterized skill. You have a measurable, trackable record of daily AI usage.
Prompt engineering and skill authoring — You can write a structured prompt (YAML \+ Markdown or equivalent) that routes correctly 95% of the time, handles edge cases gracefully, and encodes enough domain knowledge that the model behaves like a subject matter expert. You think in terms of context, instructions, examples, and output format — not just "the thing I typed before the code came out."
Python — Modern, type\-hinted, readable. You write Python\-based applications, data pipelines, and reporting automation. You understand caching, session state, and how to structure a multi\-page app cleanly.
SQL — CTEs, window functions, incremental pipeline patterns. You don't look up the syntax for a row\-numbered deduplication.
Data modeling fundamentals — You understand semantic layers, and how to build a model that a non\-technical user can query in plain English.
STRONG PLUS
- Snowflake Cortex — Cortex Analyst, Cortex Agents, AI\_SUMMARIZE, AI\_EXTRACT, Dynamic Tables, semantic views
- SnowWork / CoCo — Prior experience deploying agents, authoring skill files, or working within the Snowflake Intelligence ecosystem
- Finance literacy — You can read a revenue waterfall, distinguish ARR from NRR, and explain what drives a QoQ change in product revenue
- Reporting automation — openpyxl, multi\-tab Excel exports formatted to spec, named ranges
- dbt — Model authoring, ref() patterns, YAML tests in a cloud warehouse context
- Semantic search / embeddings — Vector similarity, embedding\-based retrieval, and how they power natural language analytics
SOFT SKILLS REQUIRED
TRANSLATES BETWEEN AI, DATA, AND FINANCE
Your stakeholders are financial analysts and senior directors who think in Excel models and board decks. You write prompts and code, but your output needs to make sense to someone who has never opened a terminal. You are the translation layer between what the model can do and what finance actually needs.
You communicate complex ideas simply, ensuring stakeholders understand, trust, and can act on what you build. You are the translation layer between what the model can do and what finance actually needs.
THINKS IN WORKFLOWS, NOT TASKS
You don't just answer a question — you build a tool that answers it forever. When asked to do something twice, you automate it. Your instinct is to encode work into a reusable agent, not to redo it manually each week.
WORKS FAST WITH HIGH ACCURACY
The role runs on a weekly cadence tied to finance deliverables. You scope, build, and ship a working artifact in 1–2 days. Accuracy matters more than speed — but accuracy is not a reason to be perpetually slow.
MINIMUM REQUIREMENTS
- 4\+ years of experience in analytics, data engineering, or a technical finance adjacent role
- Has used an AI coding assistant as a primary development tool — daily usage, not occasional
- Proficient in SQL — you can write a window function without looking it up
- Has shipped at least one Python application that end\-users actually interacted with
- Comfortable working in Git (PRs, branches, code review)
- Familiar with fiscal year concepts and core revenue metrics (ARR, bookings, NRR)
WHAT SUCCESS LOOKS LIKE AT 90 DAYS
- You've built at least two net\-new AI agents or workflow tools deployed to the Finance Analytics skill library
- You've taken ownership of the quarterly and weekly revenue analysis workflows — they run correctly on schedule without hand\-holding
- You've shipped at least one Streamlit app to production or a demo application to the Sales Field team
- You've participated in at least one quarterly earnings cycle
- Your CoCo usage is measurable, consistent, and growing week over week
WHY THIS ROLE IS UNUSUAL AT THIS LEVEL
This seat asks you to do all of that *and* build the AI infrastructure that makes the entire Finance Analytics team faster. You are simultaneously a practitioner and a workflow engineer.
If you are fluent with AI development tools, you can punch significantly above your level.
The analyst this role is backfilling ran over 22,000 AI\-assisted development sessions in their first three months. That's the pace expectation.
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 $138,000 \- $180,600\.
- 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 $138K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($159K) sits 12% below the category median. Disclosed range: $138K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Snowflake AI Hiring
Snowflake has 5 open AI roles right now. They're hiring across AI Software Engineer, AI Architect, AI/ML Engineer. Positions span Bellevue, WA, US, New York, NY, US, Menlo Park, CA, US. Compensation range: $180K - $330K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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