AI Research Developer (Mac) — Part-Time

Mercor; Toronto; On-site; CAD 55,000 - 83,000; Part time

Job summary

Mercor in Toronto is seeking software developers with 2-3 years of professional coding experience or current postgraduate CS students to support a research project with a leading AI lab. You will work through real development workflows on your Mac to benchmark and improve cutting-edge AI models.

This part-time role offers a flexible 10-20 hour per week schedule, an immediate start, and the opportunity to contribute to high-quality training data and model evaluation while gaining hands-on

Qualifications

• Own a MacBook or Mac with Apple M-series or Intel processor, macOS 15+
• Hands-on experience with one or more programming languages (e.g., Python, JavaScript/TypeScript, Swift, Java, Go)
• Comfortable with macOS developer tools such as VS Code, Xcode, Terminal, Git, and package managers
• Currently pursuing or recently completed Masters studies, or have 2-3 years of relevant experience + Bachelors from a prestigious institution
• Ability to clearly explain your reasoning and excellent written and verbal communication skills

Responsibilities

• Complete and narrate real software development tasks on your Mac to create high‑quality training data for advanced AI models
• Build, debug, refactor, and review code with a focus on benchmarks
• 10-20 hour weekly commitment with structured responsibilities and no pressure of full-time roles
• Immediate start preferred

Skills

Python

JavaScript/TypeScript

Swift

Go

Education

Master's degree in CS

Bachelor's degree

Tools

VS Code

Xcode

Terminal

Git

Package managers

Job description

Mercor in Toronto is seeking software developers with 2-3 years of professional coding experience or current postgraduate CS students to support a research project with a leading AI lab. You will work through real development workflows on your Mac to benchmark and improve cutting-edge AI models.

This part-time role offers a flexible 10-20 hour per week schedule, an immediate start, and the opportunity to contribute to high-quality training data and model evaluation while gaining hands-on