Drive DeepL's headless distribution surfaces
Engineering owns the DeepL MCP server and CLI. You own the reach of both
Partner with Engineering, Design, and Product to ensure DeepL's MCP and CLI fit naturally into how developers build, ship, and integrate agentic workflows
Drive MCP discoverability across the Claude ecosystem, ChatGPT, Microsoft Copilot, and emerging agent marketplaces
Drive CLI adoption through package registries (npm, pip, Homebrew), GitHub visibility, developer toolchains, CI/CD examples, and starter kits
Work with the Partner team to incorporate MCP and CLI surfaces into hyperscaler marketplaces and co-sell motions
Monitor the agentic tooling and developer ecosystem, identify emerging distribution opportunities, and bring ecosystem intelligence back to Product and Engineering
Own the headless activation funnel
The strategy bets on usage-led growth through MCP and CLI adoption. You own the funnel from install to paid usage
Instrument the activation funnel end-to-end, from MCP install or CLI OAuth through first successful translation and first paid character
Shape the free-tier strategy, onboarding experience, and adoption patterns that optimise conversion
Identify and remove the highest-friction step in the funnel, bringing structural issues back to Product and Engineering with evidence
Report on the headless funnel as a leading indicator of ecosystem growth and adoption
Build the artefacts developers reach for
The distribution work that compounds fastest is the repository someone forks, the starter kit they adopt, or the implementation example their coding assistant surfaces
Create prompt libraries, agent templates, implementation examples, and integration guides that make DeepL the obvious choice for agentic builders
Develop workflow documentation showing how DeepL's glossary, style, and customisation capabilities combine with agentic patterns in ways native LLM translation cannot replicate
Build code samples and reference integrations that demonstrate production-quality usage, not toy demos
Identify capabilities and opportunities that could unlock additional value for developers
Create content that demonstrates practice, not features
The scarce skill in developer content is not explaining what an API does—it is showing how thoughtful practitioners actually use it
Create 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
Show what production-grade implementations look like versus integrations that fail under real-world conditions
Write, record, and publish content across GitHub, Discord, LinkedIn, developer newsletters, and other relevant channels
Structure technical content so it is discoverable by both developers and AI coding assistants
Build distributed ecosystem presence
Developers discover tools through communities, practitioner content, and increasingly through AI assistants
Participate meaningfully in the communities where agentic developers work as a credible practitioner
Amplify developers already building with DeepL through showcases, workflow breakdowns, and collaborative content
Build relationships with creator-developers and ecosystem voices whose audiences overlap with DeepL's target builder cohorts
Strengthen DeepL's presence and trust across the surfaces where developers discover and evaluate tools
Shape the product from the outside in
Sit at the boundary between the agentic developer ecosystem and DeepL's Product and Engineering teams
Gather signal from MCP communities, GitHub discussions, AI ecosystems, and developer feedback
Identify emerging integration patterns, ecosystem needs, and gaps in DeepL's current API surface
Understand where DeepL wins and loses against native LLM translation workflows and why
Translate ecosystem insights into actionable input for the product roadmap
QUALITIES WE LOOK FOR