Software Engineer, AI Coding Tasks

Justremote; United States; Remoto; Tempo pieno

Descrizione del lavoro

Askable is seeking experienced software engineers to craft AI coding tasks from repositories you know well. Remote work, US$1,000 per task that passes review, with no cap on tasks. Propose, build, stress-test, model-test and review tasks to help train AI models.

We require 2+ years in software engineering, open-source contributions, and proficiency in Python/TypeScript/Go/Rust/Java/C++. You must use Docker, Linux and Git, write tests and rubrics, and maintain at least 10 hours weekly with a

Competenze

• 2+ years of professional software engineering.
• Code merged into a public open-source repository, ideally one you know well.
• Strong in at least one of Python, TypeScript, Go, Rust, Java or C++.
• Solid with Docker, Linux, Git and automated testing.
• Regular use of an AI coding tool.
• Experience writing tests, rubrics or evaluation criteria, ideally for AI models.
• Explain a change clearly and concisely, in your own words.
• At least 10 hours a week, starting now.
• A secure personal or work computer (no public or shared machines).

Conoscenze

Software engineering

Open-source code

Python/TypeScript/Go/Rust/Java/C++

Docker

Linux

Git

Automated testing

AI coding tools

Testing rubrics

Clear communication

10 hours/week

Secure workstation

Strumenti

Docker

Linux

Git

CI/CD

Descrizione del lavoro

About:

We're looking for experienced software engineers to build AI coding tasks from a codebase they know well. US$1,000 per task, and you can do as many tasks as you want.

You turn a real change from a repository you know into a task that today's best AI coding models struggle with: hard for the models, but fair for a skilled engineer. Your tasks are used to measure and train AI coding models.

Remote, any location. US$1,000 per task that passes review. Minimum of one task a week, about 10 hours a week.

How it works

1. Propose. Once you're selected, pick a change from a repository you know and submit a short proposal. We approve it or suggest a better candidate.
2. Build. In our platform, set up the repository as it was before the change, then write the brief, the correct solution and the grading checks.
3. Stress-test. Try lazy, incorrect and shortcut solutions and make sure your checks catch them.
4. Model testing. Run your task against leading AI models until strong models often fail but a skilled engineer can clearly solve it.
5. Review. A reviewer checks your task and you revise it. Most tasks take more than one round.
6. Next task. Once your task is accepted, start another.

Terms

• US$1,000 for each task that passes our quality review, paid directly to you
• Once your proposal is qualified, you sign Askable's contract and accept its terms
• The work is used for AI training. The change and your solution must be your own, and you must be free to license them to us
• Don't publish the work anywhere: no public pull request or public fork

Experience:

Requirements

• 2+ years of professional software engineering
• Code merged into a public open-source repository, ideally one you know well. A private codebase you own may also qualify (not employer or client code)
• Strong in at least one of Python, TypeScript, Go, Rust, Java or C++
• Solid with Docker, Linux, Git and automated testing
• Regular use of an AI coding tool
• Experience writing tests, rubrics or evaluation criteria, ideally for AI models
• You can explain a change clearly and concisely, in your own words
• At least 10 hours a week, starting now
• A secure personal or work computer (no public or shared machines)

Nice to have

• Deep expertise in one area, such as databases, compilers, distributed systems, security or infrastructure
• Experience building agent tasks or RL environments

Salary and Perks:

$1,000 USD Per Task Complete

About Askable:

Askable is the evidence layer for the AI-powered enterprise. It continuously captures, structures and makes real human insight queryable before anyone needs to ask a question, using AI moderated research with millions of verified participants from its own network.

Every finding traces back to a verified participant and a real session. Teams can query that evidence in seconds through Ask AI or directly inside tools like Claude, Copilot and Cursor through MCP.