
Code Generation Research Engineer (Remote Contract)
Listing checked September 15, 2026 · pay as published by Micro1
Overview
micro1 is hiring Research Engineers for a contract project that centers on code generation and model evaluation for a customer initiative. You will work with Python3, Java, Rust, C++, Go, or TypeScript to debug, refactor, and build features across varied codebases. Your annotations and feedback will help train next-generation AI systems, shaping how models learn and reason. No prior AI experience is needed. Domain knowledge in software engineering is what counts. The role is fully remote and starts within 24 to 48 hours after onboarding.
What You'll Do7
- 1Trace and fix bugs in codebases written in Python3, Java, Rust, C++, Go, or TypeScript.
- 2Design and build new features that support code generation workflows.
- 3Restructure existing code to improve performance, maintainability, and adaptability.
- 4Create and review realistic coding tasks used to measure AI model accuracy.
- 5Write clear annotations and feedback that guide model training and assessment.
- 6Work with open-source contributors and technical leads to set direction and keep outputs solid.
- 7Record technical decisions, best practices, and solutions so the project stays transparent and knowledge is shared.
Requirements8
- 1Background in competitive programming and coding problem analysis.
- 2Deep knowledge of at least one of: Python3, Java, Rust, C++, Go, or TypeScript, plus strong grasp of algorithms and data structures.
- 3History of open-source contributions or work on collaborative software projects.
- 4Sharp analytical skills to read complex problem constraints and weigh multiple solution paths.
- 5Excellent written and verbal communication, with the ability to explain technical details plainly.
- 6Keen eye for detail when validating code and checking output consistency.
- 7Experience working on your own in a remote, team-based setting.
- 8Dedication to producing well-documented, high-quality code under tight deadlines.
Who Should Apply
This role fits engineers who have competed in programming contests, contributed to open-source repos, and can switch between languages like Python3, Java, Rust, C++, Go, or TypeScript without friction. You should be comfortable reading unfamiliar code, spotting bugs, and writing annotations that explain your reasoning. The work is less suited to people who prefer a fixed 9-to-5 schedule or need constant supervision, since pay is per task and you must meet a weekly minimum. Candidates often get rejected when they cannot show concrete open-source or collaborative project evidence, or when their communication samples lack the precision needed for model evaluation feedback.
Salary Insight
Compensation is $50 to $100 per hour, but pay is output-based. You are paid per task that meets project specifications, and the time each task takes varies with your experience and workflow. A minimum number of submissions per week applies.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$50–$100/hr
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay falls inside that range.
Based on 558 similar roles that publish pay · 93 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 630
- Listed in last 30 days
- 293
- Remote
- 96%
Hiring most right now: micro1 (283) · Mercor (86) · SME Careers (63)
Most requested skills · share of roles
- python17%
- technical writing9%
- llm evaluation7%
- ai evaluation7%
Figures from Machine Learning & AI roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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Compensation
$50–100/hr
Required Skills
Application Tip
When you apply, include links to your open-source contributions or competitive programming profiles, and name the exact languages you know best. In your written answers, show how you break down a complex problem into constraints and solution paths, because that mirrors the annotation work you would do here.
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