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Growth Engineer

0G Labs
United StatesUnited States
Full-time
Not Disclosed
Mid-Level

Job Description

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We’re looking for a hands-on Growth Engineer who can turn ideas into experiments—and turn successful experiments into repeatable systems.

You’ll combine commercial judgment with technical execution: identifying acquisition opportunities, launching tests, building workflows, and connecting the results to sales and customer success. This is not a traditional software engineering role or a marketing-only position. You might come from marketing and have developed strong technical skills, or from engineering and have moved into growth. What matters is that you can personally build, launch, measure, and improve the work.

Run Growth Experiments

  • Find and test growth opportunities. Design and execute experiments to increase qualified lead generation across channels, from outbound and landing pages to product-led acquisition.

  • Build what you need to test your ideas. Create landing pages, acquisition tools, automated workflows, or onboarding journeys that turn a hypothesis into a working experiment.

  • Measure outcomes and share the learning. Set up funnel tracking, analyze conversion and lead quality, and communicate what worked, what didn’t, and what to try next.

  • Double down on what works. Turn successful experiments into repeatable approaches and compounding growth loops—not just one-off campaigns.

Build Systems

  • Make growth repeatable. Architect and implement end-to-end systems for lead capture, enrichment, qualification, scoring, and follow-up.

  • Own the marketing → sales → customer success handoff. Build seamless flows so contacts, data, and context move through the pipeline without fragmented ownership or unnecessary manual work.

  • Connect the tools behind our growth efforts. Set up and manage CRM integrations, workflow automation, analytics, and reporting. Use APIs, webhooks, and practical code where needed to make the systems work together.

  • Keep systems working after launch. Troubleshoot issues, improve existing workflows, and take responsibility for their ongoing reliability and usability. Be comfortable learning unfamiliar platforms independently.

What You’ll Bring

  • 3–6 years of professional experience, with hands-on work in growth, technical marketing, marketing automation, or engineering-led growth. Experience in a smaller company, with limited resources and broad ownership, is particularly relevant.

  • Professional AI or Web3 experience. You have helped build or grow an AI/Web3 product, implemented substantive AI workflows in a professional role, or delivered AI systems for clients. This should go beyond occasional AI use or content generation.

  • Demonstrated building ability. You can walk through systems you personally implemented, including the integrations, automation, data flows, and troubleshooting—not just projects you coordinated. A traditional software engineering background is not required.

  • Commercial and analytical judgment. You can define a useful hypothesis, understand funnel performance, and connect your work to lead quality, conversion, and sales outcomes—not simply traffic or engagement.

  • Independent execution and clear communication. You work effectively in English, communicate clearly in a remote team, learn new tools quickly, and take ownership beyond the initial launch.

Your toolkit might include n8n, Make, Zapier, Clay, Apollo, HubSpot, Salesforce, PostHog, GA4, or AI APIs. Python, JavaScript, and SQL can also be useful. These are examples, not a mandatory checklist—we care more about what you can build and how you approach a problem.

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0G Labs (operating under 0g.ai, legally Zero Gravity Labs, Inc.) is the premier, enterprise-grade decentralized AI operating system (deAIOS), Artificial Intelligence Layer 1 (AIL) pioneer, and high-performance Web3 data infrastructure powerhouse engineered to act as the definitive, high-velocity data availability, modular storage, and verifiable compute layer for next-generation on-chain autonomous agents, deep learning networks, and scaling dApps. Founded by technology innovators Michael Heinrich and Ming Wu, the platform completely eliminates the severe systemic friction of modern AI development—where builders are trapped in centralized, opaque black-box cloud environments and legacy blockchain scaling limits that drop momentum when processing data-heavy AI workloads—by deploying an advanced, multi-layered decentralized network. Moving far beyond traditional, passive smart contract storage networks or basic GPU marketplaces, 0G natively unifies an ultra-high-throughput data availability (DA) layer achieving a staggering 50 GB per second throughput, decentralized data storage optimized for massive training sets, trustless GPU compute marketplaces running inside hardware-secured Trusted Execution Enclaves (TEEs), and its EVM-compatible 0G Chain into a single high-availability Web3 AI stack. Powering over 400 million transactions across its testnets and anchoring an expansive ecosystem of more than 300 global partners—including major networks like Alibaba, Stanford Blockchain, and Optimism—the infrastructure handles intensive model training and real-time inference with production-hardened cryptographic precision. Validating its sector-defining authority, 0G Labs has secured more than $425 million in massive institutional financing, including a $40 million seed round led by Hack VC alongside Delphi Ventures, OKX Ventures, Samsung Next, and Animoca Brands, combined with a monumental $350 million growth round in June 2025 and an $88.88 million ecosystem foundation allocation. Under the hood, its technology core utilizes sophisticated Zero-Knowledge (ZK) validation loops, the Geth-to-Reth accessibility architecture, and its signature browser-native App Studio (app.0g.ai) built to allow developers to build, secure, and deploy live AI applications using cryptographic root hashes without relying on centralized databases. What sets 0G Labs apart is its uncompromising dedication to replacing fragile, siloed corporate infrastructure dependencies with absolute data availability scale, verified decentralized machine learning, and ledger-to-model connectivity velocity; by bridging the gap between performance-intensive structural AI computation and immediate trustless Web3 execution, the enterprise remains the definitive cornerstone of modern algorithmic decentralized intelligence and global Web3 transformation.

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