Remote Research Engineer - Code Gen & Model Evaluation

YO AI Labs

Singapore

Remote

SGD 120,000 - 180,000

Part time

33 hours ago
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Job summary

YO AI Labs is seeking a Remote Research Engineer to contribute to code generation, software engineering, and AI model evaluation. You will analyze coding tasks, review solutions, and help train next-generation AI systems through practical coding challenges.

The role emphasizes strong programming skills, independent work in a remote setting, and collaboration with technical contributors to ensure software quality and robust evaluation methods.

Qualifications

  • Strong expertise in competitive programming and algorithms.
  • Proficiency in at least one language from Python, Java, Rust, C++, Go, or TypeScript.
  • Experience with debugging, feature implementation, refactoring, and performance optimization.
  • Experience with open-source contributions or collaborative software projects.

Responsibilities

  • Analyze, debug, and resolve issues across codebases.
  • Implement new features for code generation workflows.
  • Refactor and optimize code for performance and maintainability.
  • Develop and review coding tasks to evaluate AI-generated code.
  • Provide clear feedback and annotations to support model training and evaluation.
  • Collaborate with contributors and stakeholders on software quality and direction.
  • Document technical decisions, solutions, and best practices.

Skills

Competitive programming
Algorithms
Data structures
Python
Java
Rust
C++
Go
TypeScript
Open-source contributions
Analytical thinking
Communication skills

Job description

YO AI Labs is seeking a Remote Research Engineer to contribute to code generation, software engineering, and AI model evaluation. You will analyze coding tasks, review solutions, and help train next-generation AI systems through practical coding challenges.

The role emphasizes strong programming skills, independent work in a remote setting, and collaboration with technical contributors to ensure software quality and robust evaluation methods.

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