Software Engineer, Sandbox & Agent Executor

Retool Inc.; San Francisco (CA); On-site; USD 164,000 - 270,000; Full time

Job summary

Retool Inc. is seeking an AI Engineer to 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 to shape user experiences and confidence in deployments. You’ll own agentic features across multiple product surfaces, collaborate with infra teams on runtime costs, and design prompting, context construction, retrieval, routing, and tool-use strategies for long-horizon workflows.

Qualifications

• 6+ years of professional engineering experience in production systems.
• Production experience with agentic systems, including context engineering, tool use, and evaluation frameworks.
• Hands‑on experience with sandboxing technology.
• Experience in Kubernetes or equivalent (EKS/ECS).
• Strong systems thinking with AI as augmentation, not replacement.
• Experience mentoring engineers in this domain.

Responsibilities

• Own the behavior of agentic features across product surfaces, including quality, safety, variance, and failure modes.
• Collaborate with product and infra teams on deployment, cost, and reliability of agent workloads.
• Design prompting, context construction, retrieval, routing, and tool-use strategies for long-horizon workflows.
• Build evaluation systems measuring performance via statistical signals, distributions, and trends.
• Detect, diagnose, and resolve non-deterministic failures in production.
• Partner with teams on self-hosted deployments and rollout patterns.

Skills

6+ years exp

Agentic systems

Context engineering

Tool use

Kubernetes

Sandboxing

Systems thinking

Mentoring

Tools

Kubernetes

EKS

ECS

Job description

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

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.

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.