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About This Role
Airtable is the no\-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done.
Airtable's Data Science \& Analytics team seeks an Analytics Engineer to embed within our Marketing organization. This is a high\-impact, early\-career role. You will be responsible for building the canonical data infrastructure, owning critical dashboards, and enabling Marketing stakeholders to execute faster, more confident, data\-driven decisions.
We require a genuinely AI\-native professional—not merely familiar with tools like Claude, Cursor, or ChatGPT, but one who integrates them as a core part of their daily workflow. The successful candidate will possess a full\-stack mindset, a bias for action, and deep curiosity about how marketing data drives tangible business outcomes.
About the Team
The Data Science \& Analytics team at Airtable is the company\-wide partner for building data infrastructure, metrics, and insights that directly inform decision\-making. The Marketing Analytics Engineer will embed with the Marketing organization, working directly with GTM data and Airtable's core data stack: dbt, Databricks, Looker, and Omni.
What you'll do
==================
- Canonical Marketing Data Sources
+ Design and maintain trustworthy data models for core marketing metrics, managing the full lifecycle from prototyping through production.
+ Develop and govern dbt data pipelines, establishing data integrity standards and SLAs for timely, accurate delivery across the Marketing organization.
- Critical Dashboards and Self\-Serve Tooling
+ Build and optimize dashboards that deliver real\-time, self\-serve insights across high\-priority marketing areas: campaign performance, funnel conversion, pipeline contribution, and lead scoring.
+ Drive data independence for Marketing stakeholders, eliminating reliance on ad\-hoc data requests and manual reporting.
- AI\-Native Data Infrastructure
+ Collaborate with the Marketing team and data partners to establish the AI Business Context layer for marketing use cases.
+ Lead the development of tools that facilitate natural language data access and AI\-assisted reporting for non\-technical stakeholders.
- Trusted Partnership
+ Serve as the primary data partner for marketing managers, demand generation teams, and leadership.
+ Translate complex data insights into clear business recommendations via dashboards, memos, and presentations.
- Domain Expertise
+ Achieve a comprehensive mastery of Airtable's marketing data models, existing pipelines, and BI tools (dbt / Looker / Omni) within the first 6 months, becoming the definitive internal expert.
Who you are
===============
- Must\-Have
+ Expert\-level SQL: Proven ability to write complex queries involving joins, aggregations, and window functions.
+ Proficiency with dbt or equivalent data transformation tools.
+ Experience with BI and visualization platforms (Looker, Omni, Tableau, Hex, or similar).
+ Active, demonstrated daily use of AI coding tools (Cursor, Claude, ChatGPT, Gemini). Candidates must provide specific, concrete examples of how these tools are integral to their work, moving beyond simple familiarity.
+ Mandatory use of GitHub for version control in a standard development workflow.
+ Exceptional communication skills: the ability to translate technical data findings into compelling business narratives for non\-technical leadership.
- Nice\-to\-Have
+ Python for data work (pandas, ETL scripting, or analysis).
+ Prior exposure to marketing data concepts: attribution, funnel metrics, lead scoring, or campaign performance.
+ Familiarity with CRM (Salesforce) or marketing automation platforms (Marketo).
+ Experience with Databricks or cloud data warehouses.
+ A public portfolio showcasing data or AI\-assisted engineering work (GitHub, personal projects, Kaggle).
- Full\-stack Mindset: You own problems end\-to\-end and drive the solution, even if it requires expanding the original scope.
- Bias for Action: You prioritize effective delivery over perfection, operating with a 'ship, learn, and iterate' mentality.
- Genuinely AI\-Native: AI tools are fundamental to your work process. You leverage them to write cleaner SQL, debug models faster, generate documentation, and prototype solutions, and can articulate your specific usage.
- Data Storyteller: You provide definitive business closure—framing findings as actionable recommendations, not just delivering technically correct output.
- Thrives in Ambiguity: You proactively create clarity and forward momentum, even when requirements are incomplete or rapidly changing.
Airtable is an equal opportunity employer. We embrace diversity and strive to create a workplace where everyone has an equal opportunity to thrive. We welcome people of different backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status or any characteristic protected by applicable federal and state laws, regulations and ordinances. Learn more about your EEO rights as an applicant.
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All official Airtable communication will come from an @airtable.com email address. We will never ask you to share sensitive information or purchase equipment during the hiring process. If in doubt, contact us at hr@airtable.com. Learn more about avoiding job scams here.
Salary Context
This $157K-$193K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Airtable, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($175K) sits 5% above the category median. Disclosed range: $157K to $193K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Airtable AI Hiring
Airtable has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $193K - $193K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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