Vote is a pioneering company focused on building the attention signal layer of the internet. We aim to detect emergent trends and sentiments before they become mainstream, leveraging advanced AI, an understanding of social dynamics, prediction markets, and blockchain technology. Our vision is to transform how early conviction is captured and converted into intelligence, moving beyond traditional reactive data analysis.

About the Position

Introduction

Vote is building the attention signal layer of the internet, a system designed to detect early momentum across content, narratives, creators, and markets before traditional consensus forms. This role focuses on understanding the internet proactively rather than reactively, operating at the intersection of AI, consumer social, prediction markets, on-chain data, gaming mechanics, and cultural trend formation.

We are not just building a model, but a comprehensive system that captures early conviction and translates it into actionable intelligence. We believe the most valuable signal on the internet is early belief under uncertainty, which we combine with multimodal content signals, temporal attention dynamics, user reputation, network propagation, and on-chain coordination to create a continuously updating attention graph.

This presents an interesting challenge as there's no clean dataset, no ground truth, and no single 'correct answer.' We are modeling emergence, attention formation, cultural phase transitions, and human intuition under uncertainty. This means timing, weak signals, aggregation, and reputation are paramount, and uncertainty is an inherent part of the system.

Responsibilities

As a Founding AI Engineer, you will help design the core system, with key responsibilities including:

Developing multimodal representation pipelines for text, audio, and visual embeddings.
Designing ranking and scoring systems for early signal detection.
Implementing temporal modeling of attention and velocity shifts.
Building reputation-weighted systems and calibration layers.
Creating ensemble-style architectures that integrate human and machine signals.
Working with noisy, incomplete, and real-world data systems.
Collaborating directly with the founding team to shape core architecture from day one.

Requirements

A mindset that challenges conventional ML systems, seeking to solve messy and ambiguous problems.
A strong focus on building comprehensive systems, not just isolated models.
Demonstrated experience building projects from scratch.
Ability to understand and manage tradeoffs between theoretical concepts and practical realities.
Experience in areas like recommendation systems, ranking/retrieval, multimodal ML, time-series systems, market-based systems, or distributed systems is a plus, but not strictly required if you possess the foundational problem-solving skills.

Nice to Have

Overlap with on-chain data systems, token-incentivized networks, and market design/information aggregation.

Benefits

Direct ownership of core systems.
A genuine founding role with significant impact.
Exposure to both consumer scale and data infrastructure.
The opportunity to work on innovative solutions that aim to create a new primitive on the internet.