Product Lead, Agentic Personalization

When you open Spotify, the homepage is different for every user. When you browse Nike's site, products are ranked by what Nike thinks you'll buy. When you log into Chase, offers are tailored to your spending. This is web and mobile personalization: deciding in real time what each customer sees inside your product. It directly moves conversion, retention, and revenue at the point where customers are already engaged.

Almost nobody does it well outside of companies that build it from scratch. A single experiment (testing whether high-spend users convert better seeing a loyalty upsell on the account page) requires an analyst to build the segment, a PM to write targeting rules, a designer to create variants, an engineer to instrument the test, and someone to monitor results. Most teams run 5–10 per quarter and end up with a handful of static rules covering a fraction of their users.

The tools that exist (Optimizely, Dynamic Yield, Adobe Target) handle infrastructure but don't solve the coordination problem. Every experiment, audience, and piece of content still requires manual configuration. They also force companies to duplicate customer data into a proprietary store, adding months of implementation and permanent vendor lock-in.

The shift from "AI assists a step" to "AI owns a workflow" is already happening in software engineering. Personalization is ready for the same shift.

Today: A growth team testing whether high-intent visitors convert better with a comparison page spends 2–4 weeks coordinating across tools and people. They run maybe 8 experiments that quarter.

With agents: The growth lead describes the goal. An agent identifies the audience from existing customer data, generates variants informed by past performance, configures the experiment with holdout groups, launches it, monitors significance, and reallocates traffic. The human reviews before launch. Calendar time: hours. The same team runs 50+ experiments per quarter because the coordination cost drops to nearly zero.

Agents handle the full chain as one workflow: audience, content, experiment, optimization. Instead of a bunch of manual steps, agents rethink this as a continuously improving closed loop.

More than 1,000 enterprises—like Domino’s, Chime, Spotify, Ramp, Whoop, Grammarly, use Hightouch to run their marketing. Earlier this year we launched the Agentic Marketing Platform with two pillars:

• Agentic Advertising Studio. Agents analyze performance, competitor creative, and brand guidelines, then assemble ad concepts across Meta, Google, TikTok, and LinkedIn.
• Agentic Lifecycle Studio. Agents identify lifecycle opportunities, draft audiences, assemble on-brand email and SMS campaigns, and orchestrate them through Braze, Iterable, Salesforce, and Adobe.

Advertising covers paid media. Lifecycle covers email, SMS, and push. Agentic Personalization is the third pillar: what customers experience inside the website and mobile app. Different buyer (product and growth teams), different workflow (real-time experimentation), different technical surface (browser SDKs and edge delivery). Same agent infrastructure and data foundation.

What no competitor can replicate:

• Agent orchestration. 25+ specialized skills for audience building, content generation, campaign analysis, and multi-step workflow coordination.
• Content assembly. Agents search existing asset libraries for reusable on-brand content before generating anything new, which is what makes output trustworthy enough for enterprises to ship.
• Marketing and brand context. Persistent layer connecting customer data, brand guidelines, creative assets, and performance history so agents operate grounded in how the business works.
• Real-time data infrastructure. Low-latency personalization API, sub-second audience evaluation, ML-powered optimization and recommendations, event pipeline processing millions of events daily.

Founding Product Manager for Agentic Personalization: Hightouch's third major pillar and biggest new investment. You define the product, the sequencing, and the go-to-market.

• Customer discovery. Deep engagement with product and growth teams at enterprise companies to understand how they personalize web and mobile today, and what would earn their trust in an agent-driven approach. Translate into a roadmap tied to revenue milestones.
• Define the agent experience. What agents do autonomously (segments, variants, experiments, traffic allocation) vs. what needs human approval (production launches, brand-sensitive content). Design the feedback loop that builds trust over time.
• Scale the GTM. Co-own pipeline, join customer calls, shape competitive positioning, partner with CS on implementation playbooks.

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

• Zero-to-one track record. Products you built from nothing to meaningful revenue, ideally at a B2B SaaS company. Domain matters less than evidence you can find product-market fit under uncertainty.
• Technical depth. Can evaluate architectural tradeoffs, debate infrastructure decisions with senior engineers, and reason about things like batch vs. real-time and caching strategies at the level of product decisions.
• Commercial instinct. Think in terms of pipeline, conversion, and expansion. Build roadmaps that map to revenue targets.
• Speed. Small team, high autonomy, tight cycles. We use AI heavily in our own workflows.
• Cross-functional trust. This spans engineering, design, sales, CS, and leadership. You drive clarity from ambiguity.

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $240,000 - $320,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window.