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Binance is building one of the largest internal AI agent fleets in the industry — hundreds of sandboxed agents powering automation across trading, compliance, customer service, risk, and beyond. This role sits at the core of that platform: you'll build the infrastructure and tooling that makes every agent faster, smarter, and more impactful — directly translating into operational efficiency gains and accelerating business growth.
We're looking for a strong individual contributor who stays close to the frontier and has the instinct to turn promising ideas into working implementations.
This is a builder role. You'll own the full stack from agent skill authorship to infrastructure tuning, and you'll be the person who spots a new technique in the wild and figures out how to make it real inside our platform.
Responsibilities
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
Requirements
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
Nice to have
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
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