---
title: 'Software Engineer, Sandbox & Agent Executor at Retool'
canonical: 'https://feeny.ai/job/software-engineer-sandbox-agent-executor-retool-san-francisco-ftyfs2tqw2gt'
type: 'job'
last_seen: '2026-09-24'
---

# Software Engineer, Sandbox & Agent Executor at Retool

- **Company:** Retool
- **Location:** San Francisco, CA
- **Compensation:** $164k–$306k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-18
- **Last confirmed live:** 2026-09-24
- **Apply:** https://jobs.gem.com/retool/am9icG9zdDpEp7_HGZ1MHRIeXpTgYUZm

## Job description

## Why we're looking for you

We build products where agents write and run code, and where the environment that code runs in is ours to own. As agents get more capable, users go from prompt to working app in minutes instead of hours, and increasingly they expect agents that don't just generate the app but run the work inside it.

We're looking for engineers who have shipped agentic products into production and kept them running reliably, affordably, and at scale, and who want to bring that experience to developer-facing surfaces used every day by real engineering teams.

## What you'll do

As an AI Engineer, you'll build the agent platform behind Retool's AI products and own model-driven behavior in production. You will work across the product, infrastructure, and evaluation layers. Your work shapes what users experience and how confidently the team can ship. You might:

- Own the behavior of agentic features across multiple product surfaces, including quality, safety, variance, and failure modes, and shape the tool and harness surface agents operate against, including MCP servers, sub-agents, and skills
- Work across the product and infrastructure boundary, shaping agent behavior while understanding what it costs at runtime, and serve as the infrastructure team's technical counterpart on agent workloads
- Design and evolve prompting, context construction, retrieval, routing, and tool-use strategies for long-horizon workflows, and build the evaluation systems that measure them through statistical signals, distributions, and trends rather than pass/fail tests
- Detect, diagnose, and resolve non-deterministic failures such as hallucinations, partial correctness, instruction drift, or context sensitivity, working from transcripts and traces rather than logs alone
- Partner closely with product and infrastructure teams on how agent workloads are provisioned, isolated, and rolled out, including for self-hosted customers, and set the pattern for how we ship agentic products safely

You'll work across the stack (TypeScript, Node.js, React), but your leverage won't come from code volume alone. It will come from shaping runtime behavior with precision, measurement, and intent.

What this role is, and is not It is

- Accountable for agent behavior, not just system correctness
- Designing, Building, and Deploying agentic products in both cloud and self-hosted environments
- Grounded in evaluation, iteration, and regression prevention under non-determinism
- Comfortable designing systems where outputs vary, confidence is probabilistic, and correctness is contextual It is not
- Adding LLM calls to existing features and moving on
- Shipping AI features without owning their long-term reliability, drift, or user trust
- An SRE role, though you'll own the agent-side bugs that surface as infrastructure incidents
- Model training or research, though you'll shape model behavior, selection, and tool design

## THE SKILLSET YOU’LL BRING

- 6+ years of professional engineering experience, with ownership over complex systems in production
- Production experience with agentic systems, including context engineering, tool use, and evaluation frameworks, at real user scale rather than in pilots or demos
- Hands-on experience with sandboxing technology and running agents inside sandboxed environments
- Experience in Kubernetes or equivalent in practice (EKS, ECS, or similar), owning services end to end
- Strong systems thinking, with the instinct to use AI as an augment to engineering judgment rather than a replacement for it
- Curiosity in why a model produced what it did, and the habit of checking rather than assuming
- Experience mentoring engineers on this kind of work, including when to lean on a model and when not to

## BONUS POINTS

- Experience building for developer surfaces like CLIs, IDE extensions, or coding harnesses
- Experience shipping into enterprise or air-gapped environments, where you debug systems you can't directly observe

## WHO YOU'LL WORK WITH

You'll join a small, senior team focused on advancing AI capabilities across the product. You'll collaborate closely with product engineers, infra engineers, designers, and PMs, often acting as the final owner of AI behavior and quality before features reach users. Your work will set standards that others build on. If you enjoy being the person teams rely on when AI behavior matters most, even when certainty is never guaranteed, you'll thrive here.

READY TO BUILD RELIABLE AI SYSTEMS? If you're excited to move beyond demos and take real ownership of nondeterministic behavior in production, defining quality, preventing regressions, and turning variability into a strength, we'd love to meet you.

## About Retool

## Company Overview
- **One-liner**: Retool provides a platform for building, deploying, and managing custom internal software and AI applications by connecting to any database, API, or LLM.
- **Entity Type**: Private (Series C)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2017
- **Founders**: David Hsu (CEO)

## Core Business
- **Primary industry**: Software Development, Enterprise Internal Tools, AI Application Platform
- **Target customers**: B2B, Enterprise, SMB; specifically developers, IT teams, and business operators building internal software
- **Mission or purpose**: To enable companies to build production-ready internal software and AI applications dramatically faster, with enterprise-grade security and governance, so that domain experts and developers alike can solve critical business problems.

## Products & Services
- **Retool App Builder**: A low-code platform to build custom internal apps (dashboards, admin panels, CRUD tools) by connecting to databases and APIs. Type: SaaS/Platform.
- **Retool Workflows**: A visual automation tool to build and orchestrate complex business processes and backend logic. Type: SaaS/Platform.
- **Retool AI**: An AI-powered app generation capability that transforms natural language into production-ready applications, agents, and workflows, integrating with any LLM. Type: AI Feature/Platform.
- **Retool Mobile**: A solution to build and deploy internal mobile apps from the same Retool platform. Type: SaaS/Platform.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (last round was Series C).
- **Key Metric**: Annual Revenue of $5 million (as per LinkedIn company data); Total Funding of $141.3 million.
- **Notable Investors/Partners**: Sequoia Capital (lead), Nat Friedman, John Collison, Daniel Gross. Customers include Amazon, Stripe, Brex, and Orangetheory Fitness.
- **Growth Signals**: Over 10,000 organizations use Retool. The company has automated over 100 million hours of work. It has 6 offices across the US and UK. Despite a recent headcount reduction (-11% YoY), it maintains a strong brand in the enterprise internal tools space and is aggressively pivoting into AI-powered app generation.

## Competitive Advantages
- **Enterprise Governance from Day One**: Unlike point solutions, apps built in Retool are production-ready and pass security reviews, eliminating the need for rebuilds or audit scrambles.
- **Deep Data Connectivity**: Securely connects to existing databases, APIs, and internal systems, ensuring apps run on real data with real permissions.
- **Unified Platform**: One platform to manage, orchestrate, and scale all internal apps, agents, and workflows, providing centralized visibility and governance for IT.
- **Trusted by Large Enterprises**: A strong customer base of high-profile companies (Amazon, Stripe, Brex) serves as a powerful validation of its enterprise readiness.

## Strategic Focus
- **AI as the Core Platform**: Retool is positioning itself as the "first enterprise AppGen platform," where AI is used to generate production-ready code from natural language, expanding the definition of "developer" to include analysts and operators.
- **Scaling Enterprise Features**: The company is focused on building out governance, security, and platform capabilities to support large-scale deployments across complex organizations.
- **International Expansion**: With offices in London and a German-speaking Sales Engineer role, Retool is actively expanding its presence in the European market.

## Why Work Here
- **Culture**: The company values ambition, intense curiosity, energy, and deep care. They emphasize moving fast, acting like an owner, and being both demanding and supportive.
- **Work Environment**: Hybrid model with offices in San Francisco, New York, Salt Lake City, and London. The company believes great work happens when teams collaborate in person.
- **Product Impact**: Employees are close to the product and use Retool internally, giving them a direct voice in improving it. The mission is to enable a wider community of builders to create production-grade software safely.
- **Hiring Process**: Structured, with initial recruiter/hiring manager chats, a technical evaluation or practical exercise, and a final round of 3–5 interviews. The company aims for transparency and candidate preparation.
- **Employee Sentiment**: Employer rating of 3.4/5.0 (127 reviews). Strengths include Work-Life Balance (4.1) and Culture (3.7). Compensation (3.4) and Career (3.4) are areas for potential improvement.

## Sources
1. [retool.com](https://retool.com/)
2. [retool.com/careers](https://retool.com/careers)
3. [retool.com/about](https://retool.com/about)
4. [linkedin.com/company/tryretool](https://www.linkedin.com/company/tryretool)
5. [jobs.gem.com/retool](https://jobs.gem.com/retool)

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