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Join the Global Visoma Network

Shape the Future of Artificial Intelligence

Become a core contributor. Work remotely on LLM evaluations, coding tasks, RLHF, and premium data collection to train the world's most advanced AI models.

Open Contributor Roles

We offer both remote tasks and specialized in-person data collection gigs. Select the domain that matches your expertise.

Remote High Demand
AI Safety & Alignment

LLM Response Evaluation

Analyze, rank, and improve the quality and safety of AI-generated responses for complex prompts. Work with state-of-the-art language models to ensure outputs meet the highest standards of accuracy.

Remote Active Hiring
Software Engineering

Code Evaluation

Review AI-generated code snippets for accuracy, efficiency, bug detection, and security vulnerabilities. Your expertise ensures that AI coding assistants produce production-ready code.

Remote Specialized Role
Machine Learning

RLHF Specialists

Provide Reinforcement Learning from Human Feedback to guide AI models toward safer and highly aligned behaviors. Shape how AI understands human preferences through structured ranking tasks.

Remote Active Hiring
Data Processing

Data Labelling

Categorize, annotate, and label vast diverse datasets to train high-performing machine learning models. Your precision directly impacts model accuracy across computer vision and NLP systems.

Remote Limited Spots
Research

Data Collections

Gather high-quality textual, visual, and conceptual data samples to expand AI reasoning footprints. Contribute to building the foundation datasets that power next-generation artificial intelligence.

Remote High Demand
Audio Processing

Voice Data Collection

Record domain-specific voice commands and conversations to train next-generation speech-to-text models. Help build inclusive, multilingual voice AI that understands diverse accents.

Remote Active Hiring
Computer Vision

Video Data Collection

Capture real-world video scenarios across varied environments for advanced computer vision training. Your recordings help AI understand spatial relationships and human activities.

In-Person Regional Based
Geospatial AI

Local Map Data

Perform in-person visits to specific locations to gather hyper-local data and imagery for geospatial AI. Bridge the gap between digital mapping and real-world accuracy through on-the-ground verification.

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