AI Jobs in Web3

Build, publish, and maintain OpenClaw skills — modular capability units used by hundreds of agents across the org to automate repetitive work and unlock new business capabilities

Develop CLI tooling for agent operations: deployment, diagnostics, session management, skill registry, and developer workflows

Own end-to-end AI agent harness engineering: lifecycle management, tool execution, context/session tuning, compaction strategies, model routing

Instrument the agent fleet with data pipelines and dashboards; apply data science techniques to understand token efficiency, failure modes, latency distribution, and business outcome correlation

Identify bottlenecks across the platform and drive measurable improvements in agent throughput, response quality, and cost efficiency — directly supporting user growth and retention

Track the research frontier — papers, open-source releases, community developments — and rapidly prototype integrations (new model capabilities, reasoning techniques, agentic frameworks)

Optimize LLM infrastructure: token budgeting, multi-provider routing, cost attribution, context window management

Harden agent sandboxes: credential isolation, prompt injection defense, guardrails

Partner with product and business teams to translate user growth goals into reliable, scalable agent workflows

5+ years in software/platform engineering; 2+ year hands-on with LLM or AI agent systems in production

AI Native mindset — you default to AI-assisted development, think natively in agent/tool/context primitives, and are allergic to doing manually what an agent could do

Skill & CLI development: experience building modular, composable tools or CLI utilities for developer platforms; TypeScript and/or Python fluency

Agent harness engineering: practical experience with OpenClaw, LangGraph, AutoGen, CrewAI, or equivalent orchestration runtimes

LLM infrastructure: token management, model routing, context compaction, cost optimization at scale

Data science capability: comfortable with log analysis, statistical profiling, SQL/Python for usage data; can translate raw telemetry into actionable insights that drive platform decisions

Research awareness: follows model releases, agent framework updates, and relevant literature; can quickly assess what's worth integrating and what isn't

Vibe coding: ships fast using AI-assisted workflows; iterative, pragmatic, high output-to-noise ratio with strong engineering fundamentals

Direct experience with OpenClaw — session config, hooks, cron/heartbeat architecture, skill registry (ClawHub)

Familiarity with CLI (Command Line Interface) and the agent tooling ecosystem

LiteLLM / AWS Bedrock / multi-provider proxy experience

Kubernetes/EKS: pod isolation, resource tuning, secrets management

Security engineering background: sandbox escapes, prompt injection, guardrail design