The role of an AI Growth Engineer at Typewise sits at the intersection of cutting‑edge artificial intelligence and relentless market expansion. Unlike traditional growth positions that rely heavily on paid advertising or established marketing hierarchies, this role demands a builder’s mindset: you are expected to create, test, and scale organic acquisition mechanisms powered by AI itself. The company has already proven its technology works—Fortune 500 names like Unilever and DPD deploy its agents to resolve customer requests across email, chat, and social channels at a fraction of the cost and a multiple of the quality. Yet, when a customer‑service leader searches for the best AI‑driven support platform, the incumbent giants dominate the results. Closing that visibility gap is the core mission, and success hinges on deploying clever, non‑paid tactics that exploit the very AI strengths Typewise brings to the table.
Today’s customer‑service software market is crowded with legacy platforms that have grown through heavy sales forces and brand recognition. Zendesk, Intercom’s Fin, and similar offerings benefit from entrenched procurement cycles and broad feature suites, but they often struggle to keep pace with the rapid advancements in generative AI and real‑time language understanding. This creates a strategic opening for a deep‑tech player like Typewise, whose roots lie in ETH Zurich research and whose text‑prediction engine can dramatically reduce agent workload while increasing satisfaction scores. Enterprises are under pressure to cut operational costs without sacrificing service quality, making AI‑first solutions increasingly attractive. However, technology superiority alone does not guarantee market traction; the missing piece is a growth engine that can make the right audiences aware of the product’s unique value proposition at the exact moment they are evaluating alternatives.
To make Typewise discoverable, an AI Growth Engineer must think beyond conventional SEO or content marketing and instead harness the product’s AI capabilities to generate growth loops. Imagine deploying a bot that autonomously scans public forums, GitHub issues, or Stack Overflow threads for customer‑service pain points, then crafts personalized, value‑focused responses that subtly introduce Typewise as a solution. Another avenue is to build an AI‑driven content engine that produces highly targeted blog posts, case studies, or micro‑videos based on trending search queries, continuously optimizing for relevance and engagement. The key is to treat each experiment as a hypothesis: define a clear metric (e.g., click‑through rate, demo request conversion), ship a minimal viable version, collect data, and iterate rapidly. Because the role reports directly to the founders, there is no bureaucratic lag—you can pivot based on real‑time feedback within hours rather than weeks.
The first 30 days are deliberately structured to ship tangible experiments and generate measurable signals, not to perfect a polished campaign. You might start by identifying three low‑competition, high‑intent keywords related to AI‑assisted ticket triage, then create a set of AI‑generated landing pages that dynamically adapt copy to the visitor’s industry or company size. Simultaneously, you could launch a Twitter‑or‑LinkedIn‑focused automation that monitors hashtags like #CustomerService or #CXTrends, replies with insightful commentary, and includes a subtle call‑to‑action to a Typewise demo page. Each experiment should be instrumented with UTM parameters, event tracking, and a simple dashboard that surfaces leading indicators such as time on page, scroll depth, or form initiates. The goal is not to achieve viral fame overnight but to establish a learning loop where every test informs the next, gradually uncovering which channels and messaging resonate most with the target persona.
Metrics matter, but in an early‑stage growth sprint the focus is on leading indicators that signal potential rather than lagging revenue numbers. For example, you might track the growth of organic impressions for a set of AI‑generated blog articles, the rate at which those impressions lead to newsletter sign‑ups, or the number of inbound demo requests attributed to a specific bot‑driven outreach campaign. Qualitative signals are equally valuable: sentiment analysis of comments on Reddit or Hacker News, the frequency of unsolicited mentions of Typewise in industry Slack communities, or the depth of engagement in webinars you host. By establishing a baseline and measuring delta after each iteration, you can quickly discern whether a tactic is moving the needle or needs to be abandoned. This data‑driven approach ensures that the limited runway of a three‑month sprint is spent on high‑leverage activities rather than speculative bets.
Building a sustainable growth system requires more than ad‑hoc hacks; it calls for a repeatable framework that captures ideas, prioritizes them, executes them, and feeds results back into the pipeline. A practical setup might involve a lightweight idea board (perhaps a Notion database) where each card contains a hypothesis, required resources, success criteria, and an owner. Experiments are then spun up using feature flags or sub‑domains to isolate traffic, allowing you to run multiple tests in parallel without contaminating the main site. Automation scripts—written in Python or Node.js—can handle tasks like scraping target sites, generating AI‑powered copy via Typewise’s own API, and publishing to CMS platforms. All of this is stitched together with a CI/CD‑like workflow that triggers on every commit, runs automated sanity checks, and deploys the experiment to a staging environment for review before going live. This engineering rigor transforms growth from a chaotic scramble into a disciplined, scalable process.
Working directly with the founders offers a rare advantage: immediate access to strategic decisions, budget authority, and the collective wisdom of the team that built Typewise from a research prototype to over fifty enterprise customers. David, the CEO, brings a background in Booz Allen strategy, meaning he understands how to align growth initiatives with broader business objectives and can quickly green‑light resources for promising experiments. Janis, the CTO, is a deep‑tech NLP expert who can help you leverage the underlying AI models in novel ways—whether that means fine‑tuning a model for a specific industry vernacular or constructing prompt‑chains that produce highly relevant outreach messages. The absence of a marketing VP layer eliminates bottlenecks; you can discuss an idea at a stand‑up and have a prototype running by the end of the day. This environment rewards autonomy, curiosity, and a willingness to own outcomes from ideation through measurement.
To thrive as an AI Growth Engineer at Typewise, you need a blend of technical proficiency, growth‑hacking ingenuity, and a deep curiosity about how language models can be applied to distribution challenges. Strong programming skills are essential—not necessarily to build production‑grade infrastructure at scale, but to whip up prototypes, automate data collection, and integrate with APIs. Familiarity with prompt engineering, LLM fine‑tuning, or embedding‑based retrieval will let you extract more value from Typewise’s core technology when crafting growth assets. Beyond the technical side, you must excel at experimentation design: defining clear hypotheses, choosing appropriate metrics, and resisting the temptation to chase vanity numbers. Creativity is paramount; the most successful tactics often emerge from unconventional channels like niche Discord communities, open‑source project sponsorships, or AI‑generated meme formats that speak the language of your audience. Finally, a growth mindset that treats failure as data will keep you iterating even when early experiments fall short.
The timing of this role is no accident. Enterprise adoption of AI agents is accelerating as companies seek to deflect routine inquiries, reduce average handle time, and free human agents for higher‑value interactions. Simultaneously, buyers are becoming wary of vendor lock‑in and are actively seeking alternatives that offer transparent pricing, rapid innovation, and demonstrable ROI. Typewise’s Swiss‑American heritage, combined with its deep‑tech pedigree from ETH Zurich, positions it as a trustworthy yet agile option in this landscape. However, the market’s attention is still captured by incumbents that spend heavily on brand advertising. By engineering growth that is intrinsically tied to the product’s AI strengths—such as self‑generating relevant content, autonomous community engagement, or intelligent SEO—Typewise can flip the script: let the technology itself become its best marketer. Success in this role would not only lift Typewise’s visibility but also validate a new playbook for AI‑first companies seeking scalable, organic distribution.
If you are excited by the prospect of building AI‑powered growth loops and want to prove your ability to ship impactful experiments quickly, the application process is deliberately unconventional: forego the traditional CV and instead send us something you have built. This could be a small bot that automates outreach, an automation script that pulls data from public sources and generates insightful reports, a content engine that publishes AI‑crafted articles on a schedule, or any other system that demonstrates your thinking and execution chops. Alongside your artifact, include a brief note outlining which channels you would attack first for Typewise and why—perhaps starting with developer‑focused forums, niche LinkedIn groups, or AI‑enthusiast subreddits where the target persona congregates. Show us your hypothesis, your minimal viable experiment, and the metric you would use to gauge early signals. By demonstrating that you can combine technical skill with growth creativity, you will give us confidence that you can help the next customer‑service leader find Typewise—not just the incumbents—when they search for the best AI solution.