Affine.io is hiring a

Web3 Protocol Engineer — Incentive Design / Validation Mechanisms / RL / ML

About Affine
Affine is building an incentivized RL environment that pays miners for incremental improvements on tasks like program synthesis and coding. Operating on Bittensor's Subnet 120, we’ve created a sybil-proof, decoy-proof, copy-proof, and overfitting-proof mechanism that rewards genuine model improvements. Our vision is to commoditize reasoning—the highest form of intelligence—by directing and aggregating the work of a large, non-permissioned group on RL tasks to break the intelligence sound barrier.

Overview

Affine’s competitive RL network depends on robust, incentive-aligned protocols that cannot be gamed. As a Protocol Engineer, you’ll design and implement the cryptographic, validation, and anti-sybil mechanisms that make our reward system trustworthy and tamper-resistant.

Your focus will be on building the rules of the game: incentive structures that drive genuine model improvement, and validation systems that fairly identify dominating models on the pareto frontier.

Responsibilities

• Design and implement incentive mechanisms that reward genuine model improvement.
  
  
  
• Build validation systems to detect and prevent gaming strategies, including sybil attacks, copy detection, decoy models, and overfitting.
  
  
  
• Anticipate adversarial strategies and harden protocol rules against potential exploits.
  
  
  
• Collaborate with researchers and ML engineers to encode anti-gaming defenses into the protocol itself.
  
  
  
• Develop incentive loops and validation frameworks that scale with increasing participation while maintaining fairness and decentralization.
  
  
  

Qualifications

• Strong background in distributed systems, cryptography, or blockchain protocols.
  
  
  
• Experience with incentive design, mechanism design, or adversarial machine learning.
  
  
  
• Proficiency in Python and/or Rust, with an emphasis on system-level engineering.
  
  
  
• Familiarity with reinforcement learning (RL) concepts and validation frameworks.
  
  
  
• Mindset oriented toward adversarial thinking: anticipating attack vectors and designing defenses.
  
  
  
• Experience in decentralized or open-network environments is a plus.
  
  
  

Impact

This role is ideal for engineers who think in terms of systems and adversaries. By ensuring Affine’s RL competitions remain fair, decentralized, and self-reinforcing, your work will directly enable the network to continuously push AI capability forward.