Senior Software Engineer – AI Coding Benchmarks at Mindrift – Remote | Jobyssey

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Effort estimate

Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.

Compensation

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

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

What we look for

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

Why this is hard

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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