Responsibilities

• Own the B2B web experience and conversion infrastructure
• Build the ROI calculator and value proof tools. Create interactive tools that let prospects quantify the productivity gains from voice-first workflows. These should be grounded in real customer data, not theoretical estimates.
• Optimize conversion paths end to end. From first visit to demo request to closed deal, this person owns the marketing side of the funnel and should be running experiments on copy, layout, social proof, and CTAs continuously.
• Build a repeatable case study pipeline. Identify the 10 to 15 highest-signal customer stories. Develop a standard framework for capturing them: problem, workflow change, quantified outcome. Drive the process end to end, including customer recruitment, interviews, drafting, and approval. Develop polished briefs to the content team for production.
• Create segment-specific proof assets. Not just generic testimonials. Build proof kits for each ICP segment: startup CTO, legal ops director, enterprise IT buyer, individual knowledge worker advocating internally. Each kit should include a case study, a quote card, a data point, and a competitive positioning hook.
• Develop the editorial calendar for B2B. Identify content gaps in the current online presence. Map content to the buyer journey: awareness (thought leadership on voice-first productivity), consideration (comparison guides, ROI frameworks), decision (case studies, security and compliance docs, implementation guides).
• Build for AI-native search and B2B content discovery. Traditional SEO is necessary but insufficient. This person should understand how AI Overviews, ChatGPT recommendations, and LLM citations work, and be building content that ranks in both traditional and AI-mediated discovery channels.
• Create sales enablement content. Battle cards, competitive one-pagers, objection handling guides, and executive pitch decks that the sales team actually uses. This is not about volume. It\u2019s about precision and usefulness.
• Support the B2B ads playbook. Own the creative direction, audience strategy, and continued improving performance of LinkedIn ads and other B2B paid channels. Provide the copy, messaging, and targeting input that the paid team executes against.
• Build the marketing motion on top of PLG signals. Use product usage data to identify high-value accounts in conjunction with the AE team, then layer targeted marketing on top: custom landing pages, executive gifting, event invitations. The goal is to marry bottom-up adoption with top-down engagement.

Preferred

• PLG-to-enterprise bridge building. Has personally built or significantly contributed to a B2B marketing function at a product-led growth company that was transitioning from self-serve to sales-assisted or enterprise. Understands the mechanics of land-and-expand, PQAs, and how to layer sales and marketing motions on top of bottom-up adoption.
• Customer insight to strategy translation. Can go from raw qualitative data (Gong calls, customer interviews, support tickets) and quantitative data (product usage, conversion funnels) to a clear marketing strategy with prioritized bets.
• Full-stack B2B content and conversion. Can write high-quality B2B copy (landing pages, case studies, ads, email sequences) and also build the conversion infrastructure around it (page architecture, CTAs, A/B testing, lead capture, nurture flows). Can both write and operate, comfortable in Figma or Canva making things!
• Data fluency. Comfortable with BI tools (we use Hex). Can build dashboards, pull their own data, and make decisions based on metrics rather than intuition alone.
• AI-native operations. Uses AI tools daily in their own workflow, not as a novelty but as infrastructure. Can build automated pipelines for content production, competitive monitoring, customer insight extraction, and reporting. Understands how to prompt, orchestrate, and quality-control AI outputs at scale.
• Speed and independence. Can operate with minimal direction in a fast-moving startup. Comfortable making decisions with incomplete information, shipping imperfect first versions, and iterating based on results. Does not wait for permission or consensus. Brings recommendations, not questions.