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Sticker Mule

AI agent engineer

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Remote from
🌐 Anywhere
Salary
USD 150k–250k / yr
Department
Software Engineering
Employment
Full Time
Experience
Open level
Published
Apply before
21 Oct 2026
Listing views
641
Application actions
76
Opportunity details

About this role.

Sticker Mule is building the most lucrative commerce platform on the Internet by combining software, manufacturing, and AI in one stack.

We’re hiring an engineer to build, run and manage a team of AI agents to help us innovate faster, improve performance, and better serve customers.

Work performed

  • Identify where agents can help and build them accordingly.

  • Work with department leads to assess AI needs.

  • Help connect agents to appropriate third-party and internal tools.

  • Measure results and remove agents that don’t deliver.

  • Test new tools and models as they ship and adopt what works.

  • Advise others on AI capabilities to help them perform.

Requirements

  • Experience building agents that run on their own and do meaningful work

  • Strong with Grok, Claude, OpenAI, and open source

  • Comfortable in Go, TypeScript, GraphQL, Postgres, GCP

  • Writes clearly in English

Compensation

  1. Salary: $150,000–$250,000 USD

  2. $20,000 signing bonus

  3. 4 weeks vacation + country-specific holidays

  4. Fully remote

Apply now >

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AI Summary

The role, at a glance.

This AI Agent Engineer role builds, operates, evaluates, and retires autonomous AI agents that improve internal performance and customer outcomes. The engineer will partner with department leaders to identify high-value use cases, integrate agents with internal and third-party tools, and measure business results. Core technical expectations include experience with autonomous agents, frontier and open-source models, Go, TypeScript, GraphQL, Postgres, and GCP. The position requires strong written English and the ability to translate AI capabilities into practical solutions for non-specialist stakeholders. It is a fully remote, full-time engineering role with significant ownership over experimentation and production adoption.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires end-to-end ownership of production AI-agent systems across discovery, design, integrations, evaluation, and operational decision-making. It combines rapidly changing model/tool knowledge with strong software engineering and cross-functional judgment.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive
$200,000
US market range$150k–$250k
AI insightThe disclosed yearly base salary is USD 150,000 to USD 250,000, with a midpoint of USD 200,000. This is a competitive US-market range for a senior-level AI/agent engineering position with autonomous systems, cloud, data, and cross-functional ownership; the separate USD 20,000 signing bonus is excluded from base-salary calculations.

Core skills

Skills and capabilities most closely associated with this opportunity.

AI agentsAutonomous systemsLLMsOpenAIClaudeGrokGoTypeScriptGraphQLGCP
Sample interview questions
Describe an autonomous AI agent you built that performed meaningful work without continuous human intervention.

I would explain the business problem, the agent's permitted actions, tools and data sources, orchestration logic, guardrails, and escalation path. I would also quantify outcomes such as time saved, accuracy, throughput, cost, or user adoption, and describe how I monitored failures.

How would you identify the best department to receive an AI agent first?

I would interview stakeholders and map repetitive, high-volume, measurable workflows with accessible data and a clear owner. I would prioritize use cases by expected impact, implementation effort, operational risk, and the ability to run a controlled pilot with baseline metrics.

How would you evaluate whether an agent should be improved, constrained, or removed?

I would define task-specific success metrics before launch, including completion quality, error rate, human-review rate, latency, cost, and business impact. I would compare results with a baseline, review failure patterns, and remove or redesign the agent if it cannot meet a justified quality and ROI threshold.

What design practices would you use to connect an LLM agent safely to internal and third-party tools?

I would apply least-privilege credentials, allowlisted actions, structured tool schemas, input validation, audit logs, rate limits, approval gates for consequential actions, and robust retry and rollback behavior. I would test adversarial and malformed inputs and ensure sensitive data is minimized and protected.

How do you decide among Grok, Claude, OpenAI models, and open-source models for a production agent?

I would benchmark candidate models on representative tasks using quality, reliability, latency, cost, context requirements, tool-use behavior, privacy constraints, and operational support. I would select the model that best meets the agent's measurable requirements and maintain an evaluation suite to reassess choices as models change.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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