(Founding) ML/Game Theory Lead – AI/Blockchain Platform

About wh-ai

wh-ai is building the future of knowledge, enabling collaboration of both experts and AI. Our revolutionary platform implements a peer-to-peer dialectic process on a decentralized trustless network where questions are answered and verified by the fittest and most advanced AI agents. We enable a future where we can thrive in novel and abundant organic and synthetic data, expertly verified, with immutable primitives that empower the next discovery on a solid bedrock.

We’ve just closed our first financing round (backed by some super Angels and VCs) and we’re building our strike founding team to launch the public Beta and accelerate core development. Joining now is an opportunity to make an impact in days not years, creating exponential value in the community and equity/token.

The Role

We’re seeking a (Senior+/Principal/Architect) ML/Game Theory Engineer to co-lead the technical development of our AI-powered verification platform, working side-by-side with our Full-Stack AI/Blockchain Platform Lead. This is a high-impact role for a technically exceptional individual who thrives at the intersection of advanced machine learning, game theory, and agentic system design. You will architect and implement the fine-tuning strategies for our core AI agents—Intelligence Service Providers (ISP) and Intelligence Verification Service Providers (IVSP)—that underpin trustless information verification and decentralized knowledge creation.

Your work will directly shape the mechanisms that ensure outputs are not just intelligent, but correct, comprehensible, and aligned with customer interests, leveraging the latest in LLM training, distillation, and evaluation frameworks. You will help design the incentive, verification, and equilibrium mechanisms that allow diverse AI and human agents to compete and cooperate in a robust, game-theoretic ecosystem.

What You’ll Do

ML/AI Agent Engineering

• Lead the design and implementation of fine-tuning, distillation, and evaluation pipelines for LLM-based ISP and IVSP agents, optimizing for accuracy, explicability, and alignment in a multi-agent, trustless environment.
• Integrate advanced frameworks for claim extraction, fact-checking, and output verifiability, ensuring that agent outputs are both entailed by source data and understandable in isolation.
• Develop reinforcement learning and multi-turn agent strategies to improve agent performance in adversarial and cooperative settings, drawing on the latest research in agentic deployments and multi-agent learning.
• Architect systems that manage and mitigate negative-value information feedback loops, ensuring the positive value bias of information networks and preventing runaway dynamics.

Game Theory & Mechanism Design

• Apply game-theoretic models to design incentive structures, competition protocols, and equilibrium strategies for decentralized agent networks, including Nash equilibrium analysis and coalition resistance.
• Design trustless verification and reward mechanisms that make verification easier and more robust than answer generation, leveraging concepts from mechanism design, repeated games, and evolutionary dynamics.
• Model and simulate strategic agent interactions (AI and human) to optimize for system-wide alignment, diversity of agent types, and minimization of agency costs.

System Integration

• Collaborate with the Full-Stack/Blockchain Platform Lead to integrate ML/game-theoretic components into the broader distributed verification, reward, and knowledge management architecture.
• Contribute to the design of the protocol’s core primitives: atomic chain-of-thought reasoning, verifiable step decomposition, and secure agent identity and reputation systems.

What You Bring

Required Technical Skills

• 2+ years hands-on experience in ML/AI, with deep expertise in LLM training, fine-tuning, distillation, and evaluation (OpenAI, Anthropic, Llama, etc.).
• Strong background in multi-agent systems, game theory, or mechanism design, with demonstrable experience designing or analyzing agentic interactions in adversarial or cooperative settings.
• Proficiency with ML frameworks (PyTorch, JAX, TensorFlow), agentic orchestration (LangChain, Haystack, or similar), and distributed training pipelines.
• Experience implementing or integrating claim extraction and fact-checking frameworks (e.g., Claimify), including decomposition of outputs into verifiable atomic claims.
• Familiarity with blockchain/crypto incentive systems, peer-to-peer protocols, or trustless verification architectures.

Preferred Experience

• Publication record or open-source contributions in multi-agent reinforcement learning, game-theoretic AI, or decentralized ML systems.
• Experience with reinforcement learning from human feedback (RLHF), agentic evaluation protocols (e.g., Chatbot Arena, Scale AI), or consensus mechanisms.
• Familiarity with large-scale knowledge graphs, knowledge management, or cryptographic verification of information provenance.

Soft Skills

• Startup mentality—comfortable with rapid iteration, ambiguity, and high ownership.
• Collaborative leadership—able to co-drive architectural decisions and cross-functional integration.
• Communication—can explain complex technical concepts to diverse audiences, including non-technical stakeholders.
• Problem-solving—adept at debugging, optimizing, and scaling complex distributed agent systems.

Type:

Full-time in-person Sydney (AU); or, remote Bay Area (US) – office opening soon

Experience:

5+ years, Senior+/Principal/Architect

Compensation:

Competitive equity/token + salary