Founding Harness Engineer – AI Agents

Bolder Apps

. Build the agent runtime, including context, tool choice, approval waits, and decisions about when to act, ask, or stay quiet .

Posted 10/13/2026contractRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and tuning LLM agents for production environments, with a strong focus on safe tool execution and backend development in Python or TypeScript. Capable of transforming user feedback into actionable improvements while ensuring data security and system resilience.

Highest-signal resume keywords

LLM Agent DevelopmentPython Backend DevelopmentEval and Regression Suite ImplementationSafe Tool ExecutionExcellent Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Agent DevelopmentPythonTypeScriptAsync JobsRetriesState ManagementEval and Regression TestingPrompt TuningSystem Prompt WritingData Security

Soft Skills

Good Instincts for Agent InitiativeAbility to Explain Tradeoffs

Tools & Technologies

SlackGmailNotionLinearBraintrustLangfuseLangSmith

Certifications & Qualifications

MCP

Industry Keywords

Tool ExecutionPrompt InjectionMulti-Tenant Data RiskStartup Experience

Tech Stack

Tools & technologies

PythonTypeScript

About the role

Key responsibilities & impact

Build the agent runtime, including context, tool choice, approval waits, and decisions about when to act, ask, or stay quiet
Write and continuously tune system prompts
Own the eval and regression harness and test prompt changes like code
Make long-running work resilient to interruptions, retries, and flaky APIs
Implement guardrails, approval gates for risky actions, and strict separation of customer data
Work directly with the founder and early users and turn feedback into shipped behavior

Requirements

What you’ll need

Shipped tool-using LLM agents to real users in production; demos and prompt wrappers do not count
Good instincts for agent initiative, supported by test results
Hands-on experience building eval and regression suites for LLM behavior
Understanding of safe tool execution, prompt injection, and multi-tenant data risk
Solid backend skills in Python or TypeScript, including async jobs, retries, and state
Ability to take a fuzzy problem from prototype to production and explain the tradeoffs
Excellent written and verbal communication
Nice to have: MCP or integrations with Slack, Gmail, Notion, or Linear
Nice to have: eval and tracing tools such as Braintrust, Langfuse, or LangSmith
Nice to have: experience as an early engineer at a startup

Benefits

Comp & perks

Competitive compensation + equity
A founding seat with ownership of the agent harness and influence over product behavior
A direct line to the founder and early users, with no layers in between
Remote anywhere in the US, ideally keeping hours that overlap with Pacific time
The tools and models needed to do the job properly
A small, experienced team that trusts you to run your own work
Path to full-time once it's clearly working for both sides