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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 is building the infrastructure that makes AI\-powered analytics trustworthy and scalable — and we're looking for an AI Analytics Engineer to help define what that looks like from the ground up.
This is a new role on a new team. Our Data Science \& Analytics org is standing up an AI \& Analytics Platform function to own the context layer, evaluation frameworks, and adoption strategy behind our internal AI analytics tools — including our natural\-language\-to\-SQL capabilities, Claude, and Omni Analytics. The goal: shift from a world where analysts are the bottleneck for every data question to one where the organization can self\-serve with confidence.
You'll be one of the first hires shaping this discipline. That means you won't just use AI tools — you'll build the systems that make them accurate, design the workflows that make them trustworthy, and partner across the business to drive adoption. If you're excited about working at the intersection of data engineering, LLM tooling, and business enablement — and you want to help define what the analytics engineer role becomes in an AI\-native world — this is the role.
What you'll do
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- Build and maintain context infrastructure: Translate institutional business knowledge into structured formats — business glossaries, DBT model enrichment, semantic layer definitions in Omni Analytics — so that AI tools can answer questions accurately, not just confidently.
- Design and run evaluation frameworks: Develop predefined test cases, accuracy benchmarks, and validation workflows that measure whether AI\-generated insights are trustworthy. Own the feedback loop between eval results and context improvements.
- Build and orchestrate AI agent systems: Help design, build, and iterate on the agent architectures that power our analytics tools — including prompt pipelines, tool orchestration, query routing logic, and guardrails that determine when AI should answer autonomously vs. escalate for human validation.
- Experiment and evaluate: Test prompt configurations, agent behaviors, and model outputs across different use cases — using eval results and accuracy metrics to drive continuous improvement.
- Develop internal AI tooling and workflows: Build tools and automations that improve DS\&A's own efficiency — identifying opportunities where AI can accelerate the team's work and executing on them.
- Build automated insight generation systems: Design and develop AI\-powered systems that proactively surface patterns, anomalies, and meaningful changes in business data — delivering the right insights to the right people without waiting to be asked. Think less "answer questions" and more "anticipate them."
- Drive cross\-functional adoption: Partner with GTM, Product, Finance, and other teams to onboard users, field questions, triage issues, and train stakeholders on how to get the most out of our AI\-powered analytics tools.
- Surface insights from usage patterns: Monitor query logs and user behavior to identify gaps in context coverage, recurring questions that should become standard reporting, and opportunities to expand self\-service capabilities.
Who you are
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- Technically curious and AI\-forward: You're energized by LLMs, prompt engineering, and the evolving landscape of AI tooling. You've experimented with tools like Claude, ChatGPT, or Cursor — and you're eager to build systems around them, not just use them.
- A builder at heart: You have a bias toward making things. Whether it's a prototype, a pipeline, or a quick script to test an idea — you default to building rather than theorizing. You may not have deep software engineering experience, but you're comfortable picking up new technical skills and exploring unfamiliar domains, especially with AI tooling accelerating what's possible.
- Analytically grounded: You're SQL\-proficient and have experience with modern data tools (dbt, Databricks, Snowflake, or similar). You have strong intuition for when data "looks wrong" and can validate query logic and troubleshoot issues independently.
- Not married to legacy tooling: You're more interested in what's emerging than what's established. You evaluate tools based on what they enable, not how long they've been around — and you're quick to adopt new approaches when they're better.
- A clear communicator and strong writer: Context engineering is fundamentally a writing discipline. You can translate complex business logic into precise, structured documentation that both humans and LLMs can interpret.
- Business\-minded: You're genuinely curious about how the business works — how we sell, how customers use the product, what metrics matter and why. You ask "what decision does this support?" not just "is the SQL correct?"
- Energized by building something new: AI\-powered analytics is an emerging discipline — the best practices don't exist yet. You're excited to learn as you go, experiment, iterate, and help shape the playbook rather than follow one.
- Independent and proactive: You can own workstreams end\-to\-end — from scoping the problem, to building the solution, to iterating based on feedback. You bring ideas to the table and move things forward without waiting for step\-by\-step direction.
- Experience: 2 \- 4 years in data\-related roles (analytics engineer, data analyst, data scientist, or similar), including experience partnering with business stakeholders. Experience in SaaS or tech environments preferred.
- Must\-Have Skills
+ Strong SQL proficiency and experience working with modern data tools (dbt, Databricks, Snowflake, or similar)
+ Clear, structured writing — can translate complex business logic into documentation that both humans and LLMs can interpret
+ Hands\-on experience with AI tools (Claude, ChatGPT, Cursor, or similar) beyond casual use — has applied them to build or accelerate real work
+ Cross\-functional communication — can partner with non\-technical stakeholders to understand needs, triage issues, and drive adoption
+ Builder mindset — comfortable picking up new technical skills, prototyping solutions, and iterating quickly
- Nice to have
+ Experience with BI semantic modeling (Looker, Omni Analytics, or similar)
+ Familiarity with Python and LLM APIs
+ Experience building evaluation or testing frameworks
+ Background in context engineering, knowledge management, or technical writing
+ Experience with agent architectures, prompt engineering, or AI system design
+ Familiarity with data science and ML concepts (e.g., experimentation, time series analysis, statistical modeling, clustering, anomaly detection
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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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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