Customer Success Engineer - Fully Remote

Status
Open
Remote policy
Remote
Employment type
Not stated
Salary
Not stated
Categories
Customer-Success-Engineer, Technical-Customer-Support, AI-Customer-Success, SaaS-Support-Engineer, LATAM-Customer-Success, Senior-Customer-Success-Engineer, Technical-Customer-Success-Engineer, Cloud-Customer-Success-Engineer
Source
himalayas
First observed
2026-08-13 15:51 UTC
Last seen
2026-08-13 17:51 UTC
Source claims posted
2026-08-13 15:41 UTC
Consecutive misses
5 of 10

What the posting says

About the job

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/year

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.) OR 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.

Familiarity with modern AI APIs (OpenAI, Anthropic, etc.) or how agent frameworks (LangChain, AutoGPT, etc.) function is a plus.

Application Process (Takes 20–30 mins to complete)

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Resources & Support

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Originally posted on Himalayas

Quality

Completeness: 50%
Honesty: 100%

Based on 7 observation(s).

Timeline

  1. *
    #47098 2026-08-13 15:51 UTC
    Published
  2. ~
    #49272 2026-08-13 17:51 UTC
    Modified
    • Organisation
      mercor->Not stated
  3. o
    #50945 2026-08-13 19:52 UTC
    Not seen
    Miss 1 in a row
  4. o
    #52836 2026-08-13 21:52 UTC
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  5. o
    #53774 2026-08-13 23:52 UTC
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  6. o
    #55269 2026-08-14 01:52 UTC
    Not seen
    Miss 4 in a row
  7. o
    #56017 2026-08-14 03:52 UTC
    Not seen
    Miss 5 in a row