Go Backend Engineer: AI Tool Testing (Remote)

SaidGig

New York (NY)

Remote

USD 41,000 - 124,000

Part time

5 days ago
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Job summary

SaidGig is seeking a part-time remote Go backend developer to help evaluate next-generation AI coding tools. You will build REST and GraphQL endpoints, perform rigorous validation, and document results across multiple four-day testing bursts.

You will collaborate with the research team, debug thoroughly, and contribute to performance tuning and model evaluation while protecting confidential information.

Qualifications

  • 5+ years of professional experience as a Golang developer.
  • Strong command of Go, REST APIs, GraphQL, and backend development best practices.
  • Experience with AI-powered coding tools, ideally Cursor.
  • Experience with data validation and robust error handling.
  • Excellent written and verbal communication skills.

Responsibilities

  • Develop REST and GraphQL endpoints using Go.
  • Perform data validation and implement comprehensive error handling.
  • Execute database migrations and support performance optimization.
  • Test and evaluate alpha-stage AI models in Cursor through multiple four-day testing bursts, with at least five hours of work per day.
  • Document incidents, bugs, and edge cases with detailed reports, bug traces, and annotated screenshots.
  • Share timely feedback and technical insights with the research team through Slack.
  • Complete structured surveys after each testing burst covering model usability and overall impressions.

Skills

Golang
Backend development
Communication skills
Testing & debugging
Documentation

Tools

REST APIs
GraphQL
Cursor
Data validation
Error handling

Job description

SaidGig is seeking a part-time remote Go backend developer to help evaluate next-generation AI coding tools. You will build REST and GraphQL endpoints, perform rigorous validation, and document results across multiple four-day testing bursts.

You will collaborate with the research team, debug thoroughly, and contribute to performance tuning and model evaluation while protecting confidential information.

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