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ABOUT RETOOL Nearly every company in the world runs on custom software for critical operations like tracking performance metrics, handling support workflows, building admin dashboards, and countless processes you might never have thought of. But most companies don't have the resources to properly invest in these tools, leading to a lot of old, clunky internal software, or worse, teams still stuck in manual and spreadsheet workflows. AI has changed who gets to build software. The definition of "developer" now includes analysts, operators, and domain experts creating solutions directly—and the tools they reach for are multiplying by the week. That's both an opportunity and a challenge: as more people build with more AI tools, the risk of shipping ungoverned software into production grows just as fast. At Retool, we're building the platform that makes all of it safe to ship. Build with any AI tool you want, then deploy into one place that connects to your real business data, enforces enterprise policies automatically, and lets teams create once and reuse everywhere with shared, trusted components. The cost of building software has collapsed. The cost of governing it hasn't—and that's the problem we solve. Developers and domain experts have already automated over 100 million hours of work on our platform, freeing them to focus on creative problem-solving and strategic work that drives real business value. The people closest to the problem can now build the software to solve it, safely, and within enterprise guardrails. Let's build the future together.
WHY WE'RE LOOKING FOR YOU AI is making it possible for anyone to build software in minutes. But building fast and building safely are two very different things. As development democratizes and more people across an organization create apps, agents, and workflows, the risk surface expands dramatically. The question enterprises are asking isn't "can we build it?" but rather "can we trust what's been built?" On the AI Governance team, we're solving this problem by building the foundational layer that sits between builders and data. This is the policies, access controls, audit trails, and security infrastructure that make Retool the platform enterprises trust to scale across their entire organization. Your work will span the stack, from full-stack web development to product infrastructure. You'll focus on the problems that matter most to customers with thousands of employees on Retool: What slows them down? What keeps their security teams up at night? How do we make the right thing easy and the wrong thing hard? This team is responsible for making Retool deeply trusted by our largest customers. PROJECTS YOU MIGHT WORK ON
- Data Access Control Layer: Design and build the system that enforces table-level, column-level, and row-level access controls across Retool's database connectors. You might work on: policy modeling, query rewriting to inject security constraints at the data engine, and building the admin UX that makes complex rules intuitive to configure. The goal: when a builder creates an app, the data security is already handled, automatically.
- Agentic Governance: When does human authority need to back agent actions? How does this evolve as more parts of day to day work are automated over time? What does an admin need to answer "what's the worst this agent could do?" at a glance? Design & build the primitives that ensure agents are soundly permissioned and observable.
- Automated Security Center & Admin Control Panel: Build the intelligent layer that proactively keeps Retool secure and well-governed. Surface under-authenticated resources, flag potentially dangerous access patterns, monitor usage analytics and spend, and integrate with compliance and DLP tools so security and admin teams get actionable insights instead of raw data, and Retool gets smarter about protecting customers the more they use it.
THE SKILLSET YOU'LL BRING
- 2–8 years of professional software engineering experience on high impact teams; ideally some of which has been at startups
- Experience owning technically challenging, cross-functional projects from start to finish
- Strong fundamentals across the entire stack, with a strong grasp of backend systems design, data modeling, and building reliable, scalable software
- You communicate clearly in design docs, code reviews, and cross-functional discussions
- You care about code quality, testing, and leaving the codebase better than you found it
- You're motivated by solving real customer problems, not just writing clever code
NICE TO HAVE
- Experience building authorization, access control, or security systems
- Experience with policy engines, query rewriting, or data governance platforms
- Familiarity with RBAC, ABAC, or relationship-based access control models (Zanzibar, OPA, Cedar)
- Familiarity with authentication and authorization protocols (OAuth, SAML, SCIM, or similar)
- Experience designing taming complexity in admin-facing UIs or platform management tools
WHO YOU'LL WORK WITH You'll join a team that sits at the intersection of security, platform infrastructure, and product experience. You'll work alongside engineers, product managers, and designers who care deeply about making enterprise governance feel effortless rather than burdensome. You'll also partner closely with teams across Retool because governance touches everything. We're a hard-working, passionate bunch who are motivated by collaboration, strong results, and bringing the impact of Retool to our customers. When we're in the office, we enjoy eating lunch together, and we've been known for our lively game nights. But at the root of it all, we come together to show our customers and not-quite-yet customers how Retool can make them and their companies more efficient and successful. We're building systems that the largest companies in the world will rely on to keep their data safe and their teams productive. If you want your work to be foundational, the kind of engineering that unlocks everything else, this is the team. Location: San Francisco, CA Retool offers generous benefits to all employees and hybrid work location. For more information, please visit the benefits and perks section of our careers page!
For candidates based in the United States, the pay range(s) for this role is listed below and represents base salary range for non-commissionable roles or on-target earnings (OTE) for commissionable roles. This salary range may be inclusive of several career levels at Retool and will be narrowed during the interview process based on a number of factors such as (but not limited to), scope and responsibilities, the candidate’s experience and qualifications, and location. Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Retool provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans. The base pay range for this role is $163,800 – $306,000 per year.
Retool offers generous benefits to all employees and hybrid work location. For more information, please visit the benefits and perks section of our careers page! Retool is currently set up to employ all roles in the US and specific roles in the UK. To find roles that can be employed in the UK, please refer to our careers page and review the indicated locations.
Salary Context
This $163K-$306K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
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.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Retool, this role fits into their broader AI and engineering organization.
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 the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
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.
Skills in Demand for This Role
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.
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.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($234K) sits 7% above the category median. Disclosed range: $163K to $306K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Retool AI Hiring
Retool has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Francisco, CA, US. Compensation range: $306K - $306K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
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.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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