Senior Software Engineer - AI Model Evaluation

MI10 ENTERPRISES, INC.
2 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 83K

Job location

Remote

Tech stack

JavaScript
Artificial Intelligence
Python
PostgreSQL
Redis
Software Engineering
TypeScript
React
FastAPI
Kafka
Docker

Requirements

  • 5+ years in software development
  • Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis
  • Experience writing tests (functional, integration)
  • English proficiency - B2+

Benefits & conditions

Up to $40/hr equivalent, depending on level and pace. Tasks are estimated at :20 hours each; you set your own schedule.

About the company

_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 realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history * Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent * Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient * Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust 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, Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.

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