The Problem

Every large advertiser runs the same loop: strategist reviews performance data, decides what to test next, briefs a designer, waits for assets, hands them to a media buyer who builds campaigns across Meta and Google and TikTok, launches, monitors, repeats. The process is slow because creative production, campaign management, and performance analysis happen in three separate systems, and insights travel between them through decks, Slack threads, and weekly syncs.

Companies like Nike, Uber, and PepsiCo spend millions per month on digital ads and employ entire teams just to keep this loop turning. Most teams run 10 to 15 creative tests per month because every test requires manual work across multiple tools and teams. The result: most ad spend runs against stale creative, performance degrades from fatigue, and the gap between "what's working" and "what gets made next" is measured in weeks.

Why Agents Change This

Advertising is uniquely high-leverage for AI agents because nearly every step in the workflow is now automatable, and the steps compound:

• Research. Agents can continuously monitor competitor ad libraries, auto-classify creative by format, hook, and message, and surface positioning gaps. What used to require a strategist spending hours in Meta Ad Library happens in the background, always current.
• Creative production. Generative models can now produce ad-quality images and video. The constraint has shifted from "can AI make this" to "can AI make this on-brand and on-spec for each platform." That is a solvable engineering problem, not a research problem.
• Campaign structure. Building properly structured campaigns (naming conventions, audience splits, placement targeting, budget allocation) across five ad platforms is tedious, rules-based work. Agents can do it faster and more consistently than humans.
• Optimization. Detecting creative fatigue, reallocating budget toward winners, and knowing when to rotate in fresh variants is pattern recognition across structured data. Agents excel at exactly this.
• The compounding effect. When one agent handles the full chain (research, production, activation, analysis, iteration) as a single workflow, the cycle time collapses from weeks to hours. A team that runs 15 tests per month can run 50+, not because any individual step got faster, but because the coordination cost between steps drops to nearly zero.

Today: A performance marketing team testing new concepts on Meta and TikTok spends 2 to 3 weeks per cycle.

With agents: The marketing lead describes the objective. An agent analyzes recent performance, identifies the highest-value concepts to test, generates on-brand variants across formats, builds campaigns, launches after human approval, monitors results, detects fatigue, and queues replacements. Same team, 5x to 7x the testing volume, because the human bottleneck becomes strategy and judgment, not execution.

Why Hightouch, Why Now

Hightouch built the Agentic Marketing Platform: one system where AI agents own entire marketing workflows across lifecycle (email, SMS, push through Braze, Iterable, Salesforce, Adobe), advertising (creative production and activation across paid channels), and product experiences (real-time web and mobile experimentation). All three surfaces share the same agent infrastructure, brand and customer context, and warehouse-native data foundation. An insight the ads agent learns about creative performance can inform what the lifecycle agent sends. That shared context across every marketing surface is the product.

Ads is already live, and demand is surging. Enterprise customers are generating ad creative today with agents that analyze performance across Meta, Google, TikTok, LinkedIn, and Snapchat, produce briefs grounded in competitive research and brand guidelines, and generate ready-to-ship ad concepts. Early adoption has been strong: customers who start using it quickly expand usage across campaigns and teams, and inbound interest from enterprise prospects is accelerating. The foundation works. The opportunity is building it into a platform that captures the full advertising workflow.

What Hightouch has that no competitor can replicate:

• Agent orchestration. 25+ specialized agent skills for audience building, content generation, campaign analysis, and multi-step workflow coordination, all composable into end-to-end workflows.
• Content assembly with brand trust. Agents search existing asset libraries for reusable on-brand content before generating anything new. This is what makes output trustworthy enough for enterprises to ship without heavy review cycles.
• Marketing and brand context. A persistent layer connecting customer data, brand guidelines, creative assets, competitive intelligence, and performance history so agents operate with full business context, not generic prompts.
• Warehouse-native data foundation. 62,000 workspaces, a million data syncs per day, 300+ integrations. Customer data stays in the warehouse. No proprietary data store, no duplication, no lock-in.

The Role

You will lead the Agentic Ads Platform. The foundation exists (customers generating ads today), and the opportunity is turning it into the platform that closes the full advertising loop. Four major areas of work:

• Multi-model creative generation. Today the platform generates static images. The roadmap includes video generation, motion graphics, format adaptation, and multi-model orchestration (routing to the best model for each task). Building a creative generation pipeline that produces broadcast-quality output across formats is a hard, high-value problem.
• Marketing context and brand adherence. Making agents genuinely trustworthy for enterprise creative teams. Better context engineering so agents follow brand guidelines, business rules, legal requirements, and channel-specific policies without constant human correction. The gap between "AI-generated ad" and "ad our brand team would actually approve" needs to shrink dramatically.
• Platform depth across ad channels. Deep integrations with Meta, Google, TikTok, LinkedIn, Snapchat, and others. Not just launching ads, but understanding each platform's creative specs, optimization levers, and best practices so agents can automatically adapt creative for each channel, syndicate content across platforms, and optimize placement-level performance. Think: an agent that knows a 9:16 video with text-safe zones works differently on TikTok Reels vs. Instagram Stories and adjusts accordingly.
• Best-in-class agentic creation experience. The interaction model for how marketers work with agents to go from objective to live campaign. This means the conversational interface, the review and approval workflows, the feedback loops that help agents learn what "good" looks like for each brand, and the progressive autonomy controls that let teams start with full human approval and graduate to agent-led execution as trust builds.

Day to day, you will:

• Run deep customer discovery with performance marketing and creative teams at enterprise companies. Understand their workflows, pain points, and what would earn their trust.
• Define the product roadmap tied to revenue milestones. Sequence what to build, what to compose from existing systems, and what to partner on.
• Ship product. Work directly with engineering and design on tight cycles.
• Co-own pipeline and go-to-market. Join customer calls, shape competitive positioning, partner with CS on implementation.

High-visibility role working directly with founders and executive team.

What We're Looking For

• Zero-to-one track record. You have built products from nothing to meaningful revenue, ideally at a B2B SaaS company. The domain matters less than evidence you can find product-market fit under uncertainty.
• Technical depth. You can evaluate architectural tradeoffs, reason about things like multi-model orchestration and API integration design at the level of product decisions, and hold your own in debates with senior engineers.
• Commercial instinct. You think in terms of pipeline, conversion, and expansion. You build roadmaps that map to revenue targets.
• Speed. Small team, high autonomy, tight cycles. We use AI heavily in our own workflows and expect you to do the same.
• Cross-functional trust. This role spans engineering, design, sales, CS, and leadership. You drive clarity from ambiguity.