Remote AI training and data labeling jobs

Write realistic feature and bug-fix tasks for AI coding models, plus deterministic verifiers that accept valid solutions and reject the rest. About 15 flexible hours a week, paid per accepted task on a $30–100/hour band, 100 openings; a strong testing background is the stated plus.

Maintainers of real open source repositories review LLM-generated patches against their own codebase, justify each verdict in writing, and peer-review other experts. Open globally, about 15 hours a week, paid per accepted task at a stated $150–300/hour equivalent. 10 openings.

Software engineers rebuild technical PowerPoint slides, document system architectures and engineering processes, and review technical storytelling, so AI learns to explain engineering clearly. US, UK or Canada. 7 openings, contractor, $150–350/hour.

Experienced IT and systems managers: critique IT strategy, architecture, vendor and security-compliance scenarios and write the expert input that trains AI tools for IT professionals. 5+ years leading technical teams or enterprise infrastructure. 5 openings, contractor, $60–75/hour.

Design GPU programming tasks in CUDA, WebGPU or GLSL for training LLMs on performance and architecture, including kernel profiling and C++ host code. Remote contractor, $60–95/hour paid per accepted task, 25 openings. Graphics, HPC and ML acceleration backgrounds all qualify.

Create, solve and review hard full-stack engineering tasks, debug across browser, API and database layers, judge AI-generated solutions, and port whole builds from one language to another. Global and fully remote, about 15 flexible hours a week, paid per task on a $50–100/hour band, 300 openings.

US-based systems analysts bring requirements gathering, BPMN or UML process modelling and SQL troubleshooting to the design and validation of AI training scenarios. Contractor, remote (US only), 10 openings, $60–120/hour. Five years as a systems analyst or technical consultant.

Expert C# and .NET developers with strong MySQL skills build and maintain backend services and APIs on a micro1 project framed as AI training. Remote contract, only 2 openings, $60–110/hour.

Build reinforcement learning environments where an AI agent must fix bugs, add features or optimise code using real MCP servers, with deterministic verifiers and golden solutions. About 15 hours a week, your own schedule, paid per accepted task against a $60–120/hour band. 100 openings.

Contribute real-world code, explanations and code reviews as AI training data, drawing on five-plus years in Python, Java, JavaScript/Node.js or full-stack work. A narrow $60–75/hour band, 15 openings, remote contract with documentation and architecture writing at its core.

Design reinforcement learning environments where an AI agent must use real Model Context Protocol (MCP) servers to fix bugs, build features, refactor or optimise code, with deterministic verification and golden reference solutions. About 15 hours a week, paid per accepted task on an $80–120/hour band, 100 openings.

Create, solve and review hard software-engineering tasks in real repositories, including porting whole builds between languages and reviewing AI-generated patches. Global remote contractor, $100–150/hour paid per accepted task, about 15 hours a week. A verifiable record of substantial open-source contributions is required.

GPU specialists profile and optimise CUDA kernels, refactor C++ and CUDA code, and write GLSL and WebGPU shaders on a project run with a leading AI lab. Remote contractor role, 50 openings, $60–100/hour.

Build reinforcement learning environments that test whether AI models can fix bugs, add features, refactor and optimise real code, each with a reproducible setup and a golden reference solution. Around 15 hours a week, paid per accepted task on a $100–150/hour band, 100 openings, no AI experience needed.

Package real bug fixes, features, refactors and performance problems as reproducible reinforcement learning environments with golden reference solutions for AI coding models. About 15 hours a week, paid per accepted task on a $50–100/hour band, 100 openings. Same text as a higher-paying sibling posting.

Turn your favourite open repositories into reinforcement learning environments for frontier coding models: subtle bugs, non-trivial features or performance problems, each with a reproducible setup, robust verifiers and a reference solution. About 15 hours a week, paid per task on a $50–100/hour band.

A full-time, salaried research role at micro1 designing benchmarks, rubrics, datasets and evaluation pipelines for frontier coding agents. Base salary $200,000–260,000 plus equity and benefits, remote, one opening. Three years in software engineering, ML or evaluation.

Build reinforcement learning environments that test whether an AI model can deploy, secure, scale and recover production cloud infrastructure: realistic scenarios, deterministic tests, golden solutions and deliberately broken variants. 20 hours a week, paid per accepted task, $50–100/hour band.

Write hard feature and bug-fix tasks for AI coding models, plus deterministic verifiers that accept any valid solution and reject the rest. About 15 flexible hours a week, paid per accepted task on a $30–100/hour band, 300 openings; security backgrounds are a plus.

Experienced IT leaders plan and run a small technology initiative with an external vendor, SLAs and stakeholder reporting, generating realistic management scenarios to train AI. Contractor, remote, 50 openings, $70–130/hour. Six years leading multi-quarter tech initiatives is the bar.

Build features, fix hard bugs and refactor legacy code inside existing open source projects, with clear write-ups of each solution, as training input for AI coding models. Expert level in two languages plus open source and competitive programming experience. 37 openings, paid per task on a $50–100/hour band.

Build, tune and evaluate ML models in Python with MongoDB-backed data pipelines for a customer AI training project, and document every experiment. Remote contractor, $80–140/hour, 35 openings. scikit-learn, TensorFlow or PyTorch plus hands-on MongoDB is the core stack.

Build reinforcement learning environments that test whether an AI model can fix bugs, add features, refactor code and tune performance in real repositories, then write the golden reference solution it is graded against. Public open-source contributions are the requirement, not AI experience. Remote contractor work, roughly 15 hours a week.

Build reinforcement learning environments that test whether an AI agent can fix bugs, implement features, refactor and optimise code while discovering and reasoning over information from real MCP servers. You design the reproducible environment, the deterministic verification and the golden reference solution. Remote contractor work, roughly 15 hours a week, hours of your choosing.