Systems Design Expert - Fully Remote

Base Pay Range – $45.00/hr – $80.00/hr

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position
: AI Model Evaluator

Type
:
Full-time or Part-time Contract Work

Compensation
: $45–$80/hour

Commitment
:
Remote

Fluent Language Skills Required
:
English

• 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.
• Execute code and validate outputs using appropriate tools to ensure accuracy.
• 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.

• Must‑Have
  • BS, MS, or PhD in Computer Science or a closely related field.
  • Significant real‑world experience in software engineering or related technical roles.
  • Expertise in at least one relevant programming language (e.g., Python, Java, C++, JavaScript, Go, Rust).
  • Ability to solve Hacker Rank or Leet Code Medium and Hard‑level problems independently.
  • Experience contributing to well‑known open‑source projects, including merged pull requests.
  • Significant experience using LLMs while coding and understanding their strengths and failure modes.
  • Strong attention to detail and comfort evaluating complex technical reasoning, identifying subtle bugs or logical flaws.
• Preferred
  • 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.

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