Developer Growth, API & Agentic AI Products

Обязанности

• 01Drive DeepL's headless distribution surfaces
• 02Partner with Engineering, Design, and Product to ensure DeepL's MCP and CLI fit naturally into how developers build, ship, and integrate agentic workflows
• 03Drive MCP discoverability across the Claude ecosystem, ChatGPT, Microsoft Copilot, and emerging agent marketplaces
• 04Drive CLI adoption through package registries (npm, pip, Homebrew), GitHub visibility, developer toolchains, CI/CD examples, and starter kits
• 05Work with the Partner team to incorporate MCP and CLI surfaces into hyperscaler marketplaces and co-sell motions
• 06Monitor the agentic tooling and developer ecosystem, identify emerging distribution opportunities, and bring ecosystem intelligence back to Product and Engineering
• 07Own the headless activation funnel
• 08Instrument the activation funnel end-to-end, from MCP install or CLI OAuth through first successful translation and first paid character
• 09Shape the free-tier strategy, onboarding experience, and adoption patterns that optimise conversion
• 10Identify and remove the highest-friction step in the funnel, bringing structural issues back to Product and Engineering with evidence
• 11Report on the headless funnel as a leading indicator of ecosystem growth and adoption
• 12Build the artefacts developers reach for
• 13Create prompt libraries, agent templates, implementation examples, and integration guides that make DeepL the obvious choice for agentic builders
• 14Develop workflow documentation showing how DeepL's glossary, style, and customisation capabilities combine with agentic patterns in ways native LLM translation cannot replicate
• 15Build code samples and reference integrations that demonstrate production-quality usage, not toy demos
• 16Identify capabilities and opportunities that could unlock additional value for developers
• 17Create content that demonstrates practice, not features
• 18Create content that demonstrates where developers verify output, when they trust it, and how they combine DeepL with agentic workflows to achieve results native LLM translation cannot reproduce
• 19Show what production-grade implementations look like versus integrations that fail under real-world conditions
• 20Write, record, and publish content across GitHub, Discord, LinkedIn, developer newsletters, and other relevant channels
• 21Structure technical content so it is discoverable by both developers and AI coding assistants
• 22Build distributed ecosystem presence
• 23Participate meaningfully in the community