8 months ago
Remote, WorldwideSenior
Responsibilities
- Architect and build high-performance AI agent infrastructure from the ground up.
- Design and implement on-chain coordination systems for funding, governance, research incentives, and token flows.
- Build fault-tolerant backend services for agent orchestration, data pipelines, and integrations with cloud labs, bio-databases, and CROs.
- Optimize performance, throughput, and reliability for real-world research workloads.
- Lead technical decisions concerning system design, data models, and scaling strategy.
- Translate research requirements into production systems with product and science teams.
- Establish practices for code quality, testing, observability, and incident response.
- Mentor other engineers as the team grows.
Requirements
- Proven experience building AI agent systems, research platforms, or complex technical infrastructure from scratch.
- Strong background in distributed systems and high-throughput architectures.
- Expertise in one or more of Python, Go, Rust, or TypeScript.
- Deep understanding of LLM architectures, agent orchestration patterns, and production ML systems.
- Experience integrating systems with messy, real-world data sources.
- Understanding of smart contract development and on-chain coordination mechanisms.
- Experience working effectively in an early-stage, fast-moving environment.
- Strong communication skills and ability to collaborate cross-functionally.
- Experience scaling AI systems to production for thousands of users is preferred.
- Prior work in biotech, computational biology, scientific computing, DeSci, crypto protocols, or decentralized coordination is preferred.
- Open-source contributions in AI, research tooling, or infrastructure are preferred.
Benefits
- Remote-first culture
- Meaningful equity/token component
- Foundational technical ownership and impact
- Opportunity to build infrastructure for open science from day one