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Role Description

Mercor partners with leading AI teams to improve the quality, usefulness, and reliability of general-purpose conversational AI systems. These systems are used across a wide range of everyday and professional scenarios, and their effectiveness depends on how clearly, accurately, and helpfully they respond to real user questions.

In coding and software engineering contexts, conversational AI systems must demonstrate correct reasoning, strong problem-solving ability, and adherence to real-world engineering best practices. This project focuses on evaluating and improving how models reason about code, generate solutions, and explain technical concepts across a variety of programming tasks and complexity levels.

What You’ll Do

Evaluate LLM-generated responses to coding and software engineering queries for accuracy, reasoning, clarity, and completeness
Conduct fact-checking using trusted public sources and authoritative references
Conduct accuracy testing by executing code and validating outputs using appropriate tools
Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies
Assess code quality, readability, algorithmic soundness, and explanation quality
Ensure model responses align with expected conversational behavior and system guidelines
Apply consistent evaluation standards by following clear taxonomies, benchmarks, and detailed evaluation guidelines

Who You Are

You hold a BS, MS, or PhD in Computer Science or a closely related field
You have significant (5+ years) real-world experience in software engineering or related technical roles
You are an expert in one of the mentioned programming language
You are able to solve HackerRank or LeetCode Medium and Hard–level problems independently
You have experience contributing to well-known open-source projects, including merged pull requests
You have significant experience using LLMs while coding and understand their strengths and failure modes
You have strong attention to detail and are comfortable evaluating complex technical reasoning, identifying subtle bugs or logical flaws

Nice-to-Have Specialties

Prior experience with RLHF, model evaluation, or data annotation work
Track record in competitive programming
Experience reviewing code in production environments
Familiarity with multiple programming paradigms or ecosystems
Experience explaining complex technical concepts to non-expert audiences

What Success Looks Like

You identify incorrect logic, inefficiencies, edge cases, or misleading explanations in model-generated code, technical concepts, and system design discussions
Your feedback improves the correctness, robustness, and clarity of AI coding outputs
You deliver reproducible evaluation artifacts that strengthen model performance
Mercor customers trust AI systems to assist reliably with real-world coding tasks

Application Links

Rust \- https://t.mercor.com/19jq2
PowerShell - https://t.mercor.com/Tbg0V
Ruby - https://t.mercor.com/oyiCP
Bash - https://t.mercor.com/v7t4t
Swift - https://t.mercor.com/tunmS
Kotlin - https://t.mercor.com/gjh6O
R - https://t.mercor.com/BAFUA
Golang - https://t.mercor.com/dSqr7
C - https://t.mercor.com/a31gk
C++- https://t.mercor.com/TUBDU
C# - https://t.mercor.com/S8en3

[Hiring] [Remote] [Global] - Software Engineering Experts 5+ years experience (Rust, PowerShell, Ruby, Bash, Swift, Kotlin, R, Golang, C, C++, C#) $60 - $100/hour — Tech Jobs | ScrollGigs