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Software Engineer

  • Canada

Contract

This opening has closed.

The original listing is kept below for reference.

Job details

Job type
Contract
Experience
5+ years of hands-on software engineering experience
Working hours
20 hours per week
Posting language
English
Posted

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Original job description

Archived listing. The details below describe a past opening.

Start Date: Immediate

Availability: At least 4 hours per day and a minimum of 20 hours per week with an overlap of 4 hours with PST

About Client

Our client is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.

About the Role

We’re looking for experienced, hands-on software engineers to help evaluate and improve AI coding models.

Rather than primarily building production applications, you’ll work with coding agents across real-world repositories and assess the quality of their work. You’ll review generated code and agent behavior, determine whether solutions are technically correct, identify failure modes, and create the evaluation signals and feedback used to improve model performance.

Think of the coding agent as another engineer whose work you’re reviewing: Can it understand the task? Did it choose the right approach? Is the resulting code correct, robust, and maintainable? Can you explain precisely where it succeeded or failed?

What You’ll Do

  • Evaluate AI-generated code and solutions across real-world software repositories
  • Review agent behavior, tool usage, and code changes for correctness and quality
  • Identify technical errors, weak approaches, and recurring model failure modes
  • Compare model outputs and explain why one solution is better than another
  • Create and refine rubrics and evaluation criteria for coding tasks
  • Produce high-quality evaluation and preference data used to improve coding models
  • Build and maintain pipelines and infrastructure supporting data generation, collection, and evaluation workflows
  • Synthesize findings from data work into clear write-ups, updates, and recommendations for the team
  • Collaborate closely with researchers and engineers to translate qualitative judgment into scalable processes
  • Share clear, actionable findings with AI researchers and engineers

What We’re Looking For

  • 5+ years of hands-on software engineering experience
  • Strong proficiency in Python, TypeScript/JavaScript, Go, or another major production language
  • Experience working in substantial real-world codebases
  • Strong code-review skills and technical judgment
  • Ability to clearly explain why an implementation is correct, incorrect, or could be improved
  • Strong written communication
  • Experience using modern LLMs or AI coding tools

Experience with LLM evaluation, coding agents, RLHF, preference data, rubric design, or post-training is a plus, but not required.

More about this job

Responsibilities

Evaluate AI-generated code and coding-agent behavior in real-world repositories, identifying correctness issues, weak approaches, and recurring failure modes. Develop evaluation rubrics and data workflows, compare model outputs, and communicate actionable findings to researchers and engineers.

Requirements

Requires at least five years of hands-on software engineering experience, proficiency in a major production language, experience with substantial codebases, and strong code-review, technical judgment, and written communication skills. Experience with modern LLMs or AI coding tools is expected; LLM evaluation, coding agents, RLHF, preference data, rubric design, or post-training experience is a plus.

Skills

  • Software Engineering
  • Python
  • TypeScript
  • JavaScript
  • Go
  • Code Review
  • Technical Judgment
  • AI Coding Tools
  • LLM Evaluation
  • Coding Agents
  • Rubric Design
  • Preference Data
  • Pipeline Development
  • Evaluation Criteria
  • Written Communication

Visa sponsorship

Not detected in the job text

Categories

  • Software
  • Technology
  • Engineering
  • Data & Analytics
  • Science & Research

Keywords

  • Artificial General Intelligence
  • AI Coding Models
  • Coding Agents
  • Python
  • TypeScript
  • JavaScript
  • Go
  • Software Engineering
  • Real-World Repositories
  • Code Review
  • Code Quality
  • Technical Correctness
  • LLM Evaluation
  • RLHF
  • Preference Data
  • Rubric Design
  • Post-Training
  • Evaluation Criteria
  • Data Generation
  • Data Collection
  • Evaluation Workflows
  • Pipelines
  • Infrastructure
  • Model Failure Modes
  • AI Research

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