Mindrift
AI
FreelanceAgentEvaluationEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Freelance Agent Evaluation Engineer at Mindrift. Skills: Software development, Test automation, AI agent evaluation. Create challenging tasks for AI coding agents. Define evaluation criteria for AI coding agents”
What You'll Achieve.
Tasks must be submitted by the deadline; Tasks must meet the listed acceptance criteria to be accepted
Industry & Context.
Deeply understand where models fail; Identify scenarios that reveal the difference between good and bad solutions; Writing tests that accept all correct solutions and reject incorrect ones
What They're Looking For.
Must Have
Degree in Computer Science, Software Engineering, or related fields, 5+ years in software development, Primarily Python (FastAPI, pytest, async/await, subprocess, file operations), Background in full-stack development, Experience building React-based interfaces (JavaScript/TypeScript), Experience building robust back-end systems, Experience writing tests (functional, integration), Docker containers, Familiarity with infrastructure tools (Postgres, Kafka, Redis), CI/CD understanding (GitHub Actions as a user: triggers, labels, reading results), English proficiency - B2
Nice to Have
Expert in every item is not required, but comfort reading and reasoning about code across the stack is expected.
What You'll Do.
Create challenging tasks for AI coding agents
Define evaluation criteria for AI coding agents
Build virtual companies (codebase
context) for simulated environments
Assemble and calibrate tasks from intermediate states of virtual companies
Craft prompts for tasks
Design tasks in isolated environments (emulations of a developer's workstation)
Write tests that accept all correct solutions and reject incorrect ones
Iterate with an AI agent on tests
Review code written by agents
Analyze why an agent failed or succeeded
Design edge cases and adversarial scenarios
Iterate based on feedback from expert QA reviewers
How You'll Work.
Team & Collaboration
Iterate with an AI agent on tests; Iterate based on feedback from expert QA reviewers
Full Job Description
_Please submit your CV in English and indicate your level of English proficiency._ Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. ### **What this opportunity involves** We're building a dataset to evaluate AI coding agents — how well a model handles real-world developer tasks. You'll create challenging tasks and evaluation criteria within realistic simulated environments: * Build virtual companies following a high-level plan - codebase, infrastructure, and context (conversations, documentation, tickets) that form a realistic environment with development history * Assemble and calibrate tasks from intermediate states of the virtual company: craft the prompt, define evaluation criteria, and ensure the task is solvable and the evaluation is fair * Design tasks set in isolated environments - emulations of a developer's workstation: a Linux machine with development tools (terminal, CLI), MCP servers (repository, task tracker, messenger, documentation, etc.), and a real web application codebase * Write tests that accept all correct solutions and reject incorrect ones - neither too strict (breaking on valid approaches) nor too lenient (passing bad ones) * Iterate with an AI agent on tests - verifying they catch real problems, don't miss bad solutions, and don't break on good ones * Review code written by agents, analyze why an agent failed or succeeded, and design edge cases and adversarial scenarios * Iterate based on feedback from expert QA reviewers who score your work on quality criteria **What this is NOT** * Not data labeling * Not prompt engineering * Not writing code from scratch - the agent writes most of the code; you guide and evaluate A significant part of the work is done together with AI - it's very hard to create tasks that challenge frontier models without using frontier models. ### **What we look
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