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.
