Remote AI Training Engineer — Shape the Next-Gen Code

Alignerr Corp.

Portland (OR)

On-site

USD 69,000 - 138,000

Part time

1 hour ago
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Job summary

Alignerr Corp. is seeking Software Engineers for a fully remote, flexible contract in Portland. You will build, evaluate, and improve AI-generated code, helping train AI systems to think like real developers.

This hands-on role requires explaining your reasoning, writing clean code, and delivering expert feedback on AI outputs. The position is an hourly contract with 10–40 hours per week, offering independence and flexible scheduling.

Qualifications

  • Proficient in one or more programming languages listed above.
  • Solid understanding of data structures, algorithms, and software design principles.
  • Able to read, write, and reason about code with clarity and precision.

Responsibilities

  • Write, debug, and optimize code across a range of languages and problem domains.
  • Review and evaluate AI-generated code for correctness, efficiency, readability, and best practices.
  • Identify bugs, logic errors, security issues, and performance bottlenecks in AI outputs.
  • Provide structured, detailed feedback explaining why code works — or doesn't.
  • Design challenging coding prompts and edge-case scenarios to stress-test AI capabilities.
  • Compare and rank multiple AI-generated solutions based on engineering quality.
  • Work independently and asynchronously — fully on your own schedule.

Skills

Python
JavaScript
Java
C++
Go
TypeScript

Education

Bachelor's degree in Computer Science, Software Engineering, or related field

Job description

Alignerr Corp. is seeking Software Engineers for a fully remote, flexible contract in Portland. You will build, evaluate, and improve AI-generated code, helping train AI systems to think like real developers.

This hands-on role requires explaining your reasoning, writing clean code, and delivering expert feedback on AI outputs. The position is an hourly contract with 10–40 hours per week, offering independence and flexible scheduling.

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