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
$ 83KJob 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.