Customer Success Engineer - Ai Saas

Mercor; Santiago; A distancia; CLP 34.014.000 - 48.591.000; A tiempo parcial

Descripción de la vacante

Mercor is seeking a Customer Success Engineer (LatAm) for a remote, contract role with substantial overlap with Pacific Time. You will debug web apps, support customers, and help optimize AI-enabled SaaS products.

You should have a CS/SE degree, 2–5 years in customer-facing web app support, and experience with React, Node, Flask, Next.js, and AI tooling. Strong communication and problem-solving are essential.

Formación

• Ability to debug web applications.
• Degree in Computer Science, Software Engineering, or related technical field from a top-tier institution or prior experience at a high-growth technology startup.
• Experience building modern web applications (React, Node, Flask, Next.js, etc.).
• 2–5 years of experience supporting customers on such web applications.
• Comfortable with AI systems. Experience with LLMs, agents, or generative models.
• Experience exploring behavior of AI tools (fine-tuning, prompt chains, chain-of-thought debugging, or building agents).
• Ability to understand model outputs, failure modes, hallucinations, and feedback loops.

Responsabilidades

• Investigate talent-reported issues end-to-end. Reproduce bugs, identify root causes, and separate UX friction, model edge cases, and system defects.
• Debug across our AI + SaaS stack using telemetry, logs, network inspection, and database queries to understand production behavior.
• Triage with sound judgment. Escalate true engineering issues and resolve others via configuration, prompt refinement, or clear user guidance.
• Surface systemic patterns and product risks to engineering and product leadership.
• Create clear documentation and runbooks to reduce repeat issues and improve resolution speed.
• Communicate with precision, professionalism, and empathy.

Conocimientos

Debugging web apps

Web application troubleshooting

Strong communication

Problem-solving

Cross-functional collaboration

Technical writing

Understanding AI systems

Empathy and customer-facing skills

Educación

Degree in Computer Science or Software Engineering

Herramientas

React

Node.js

Flask

Next.js

OpenAI API

LangChain

Descripción del empleo

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Customer Success Engineer (LatAm)
Type: Contract
Compensation: $35,000–$50,000
Location: Remote
Commitment: Substantial overlap with Pacific Time (PT/PST)

Role Responsibilities

• Investigate talent-reported issues end-to-end. Reproduce bugs, identify root causes, and separate UX friction, model edge cases, and system defects.
• Debug across our AI + SaaS stack using telemetry, logs, network inspection, and database queries to understand production behavior.
• Triage with sound judgment. Escalate true engineering issues and resolve others via configuration, prompt refinement, or clear user guidance.
• Surface systemic patterns and product risks to engineering and product leadership.
• Create clear documentation and runbooks to reduce repeat issues and improve resolution speed.
• Communicate with precision, professionalism, and empathy.

Qualifications

Must-Have

• Ability to debug web applications.
• Degree in Computer Science, Software Engineering, or a related technical field from a top-tier institution or prior experience at a high-growth technology startup.
• Experience building modern web applications (React, Node, Flask, Next.js, etc.).
• 2–5 years of experience supporting customers on such web applications.
• Comfortable with AI systems. Experience with LLMs, agents, or generative models.
• Experience exploring behavior of AI tools (fine-tuning, prompt chains, chain-of-thought debugging, or building agents).
• Ability to understand model outputs, failure modes, hallucinations, and feedback loops.

Preferred

• Familiarity with how modern AI APIs work (OpenAI, Anthropic, etc.) or how agent frameworks (LangChain, AutoGPT, etc.) function.