AI Special Projects Engineer
L
Lightning LabsFinTech
US time zones, US time zonesFull-TimeMiddle
Salary not disclosed
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Job Details
Requirements
- Passion for bitcoin, Lightning, and the mission of bringing financial freedom to the world.
- Demonstrated ability to build and ship production-quality software using AI-native development tools such as Claude Code, Cursor, Codex, or similar.
- Strong portfolio of projects, demos, or products you have built.
- Comfortable working across the full stack and picking up unfamiliar codebases, languages, or frameworks quickly.
- Proactive and self-directed, with a bias toward action and a track record of delivering in ambiguous, fast-moving environments.
- Excellent communicator who can translate between technical and non-technical contexts.
- Familiarity with agentic AI patterns: tool use, MCP, multi-step workflows, and integrating LLMs into production systems.
- Understanding of Bitcoin, the Lightning Network, and related protocols such as L402 or Taproot Assets (preferred).
- Experience building developer-facing demos, sample applications, or technical content (preferred).
- Prior contributions to open source projects (preferred).
- Experience working at a small, high-growth startup where wearing many hats was the norm (preferred).
Responsibilities
- Ship features, tooling, and integrations across Lightning Labs products including L402, Taproot Assets, Terminal Web, LND, and new products.
- Build and maintain agent-facing tooling that makes Lightning Labs protocols and APIs accessible to AI agents and agentic workflows.
- Create compelling, working demos that showcase end-to-end flows for developers and builders.
- Identify high-signal use cases and translate them into tangible prototypes.
- Partner with business development, marketing, product, and engineering teams to identify workflows that can be automated or augmented with AI.
- Build internal tools and scripts that reduce manual overhead.
- Operate with minimal direction, identify impact areas, and execute.
- Context switch across projects and teams while maintaining high quality and consistent delivery.
- Work closely with engineers, product, and business development to understand project context and contribute meaningfully.
- Communicate progress, tradeoffs, and blockers clearly and proactively.
- Share learnings and patterns from AI-assisted development with the broader team.
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