Growth Product Manager
Embedding VC
Growth Product Manager
Embedding VC
Early-stage venture fund for Generative AI
Growth Product Manager, Product Marketing
Xnurta · Bay Area (hybrid) · Full-time
Xnurta, is an AI-native retail media and advertising optimization platform built for brands, sellers, and agencies on Amazon and beyond. We combine multi-agent AI systems, advanced predictive analytics, and deep domain expertise to deliver unparalleled advertising performance.
Thousands of advertisers run real budget through our platform every day. We are past the “does this work” stage and squarely in the “how fast can we grow this” stage — which is exactly why this role exists.
Humble Excellence – Lead with humility, execute with mastery.
Always Learning – AI evolves fast; so do we.
Extreme Accountability – Own outcomes end-to-end.
Diverse Thinking – Cross-industry, cross-domain perspectives fuel innovation.
Customer Obsession – Every model, feature, and insight must create tangible value for advertisers.
We are hiring a Growth Product Manager who owns the whole path from “never heard of Xnurta” to “can’t run ads without it.”
This is a hybrid role on purpose. Half of it is growth product: you own the funnel as a product surface — signup, onboarding, activation, first value, expansion — and you ship against it with engineering and design. The other half is product marketing: you decide what we say about the product, why it beats the alternative, and how every launch actually lands with advertisers and with our own sales and CS teams.
If you have only ever written positioning docs, or only ever run A/B tests on a signup flow, this will stretch you. That is the point. Growth at an AI-native company is one loop, not two departments.
Own the activation and expansion funnel end-to-end — signup, connect-your-ad-account, first optimization, first measurable lift, second seat, second brand.
Instrument it honestly. Find the three steps where people actually drop, and go fix them with engineering rather than explaining them in a deck.
Run a real experiment cadence: hypothesis, sample size, ship, read, kill or scale. We want a track record of shipped tests, not a backlog of proposed ones.
Build self-serve paths into a business that has historically been sales-led, and be clear-eyed about where self-serve stops and a human should take over.
Own how Xnurta is described — category, narrative, the one sentence a busy brand manager repeats to their boss.
Turn genuinely hard technical capability (multi-agent optimization, predictive pacing, generative insight) into claims that are specific, provable, and free of AI theater.
Keep a live view of the competitive set — Pacvue, Skai, Perpetua, Amazon’s own tooling — and know exactly where we win, where we lose, and what to do about it.
Price and package with product and finance. Packaging is a growth lever, not a spreadsheet exercise.
Run launches end-to-end: tiering, narrative, assets, timing, enablement, in-product announcement, and a post-launch read on adoption.
Define what “success” means before launch day, then publish the number afterwards — good or bad.
Arm sales and customer success with material they choose to use: demo flows, objection handling, proof points, competitive battlecards.
Talk to advertisers every week. Bring the raw quotes back, not the sanitized summary.
Feed what you learn into the roadmap with enough evidence that product changes direction because of it.
Be the person in the room who can say what the market will pay for and why.
Month 1: Full audit of the funnel with real numbers attached. You can tell us where every dollar and every signup leaks.
Month 2–3: A rewritten positioning and messaging framework that sales adopts voluntarily, plus your first three shipped growth experiments.
Month 4–6: A measurable lift in activation or expansion that you can defend in front of the exec team, and one flagship launch run start to finish on your standards.
3–5 years across growth product management and/or product marketing, with meaningful depth in both — or the clear appetite and aptitude to build the missing half fast.
You have personally shipped growth work: funnel instrumentation, onboarding redesigns, pricing or packaging changes, experiments with real statistical rigor. Not just overseen it.
You write well. Short sentences, specific claims, no filler. We will ask for a writing sample and we will read it closely.
You are comfortable in the data yourself — SQL, product analytics, dashboards — and do not wait on an analyst to form a point of view.
B2B SaaS experience where the buyer is a professional with a budget and a spreadsheet, not a consumer with an impulse.
You are opinionated and you update your opinions when the data disagrees.
Ad tech, martech, retail media, or e-commerce — especially Amazon Ads, DSP, or agency-side experience.
Hands-on with AI products: you have shipped and marketed something where the model is the product, and you know how to build trust rather than hype.
Product-led growth experience inside a company that was previously sales-led. You know what actually breaks in that transition.
Mandarin proficiency — much of our product and engineering org is in China, and you will work with them directly.
Startup experience preferred
Bay Area based, hybrid. This role involves regular in-person time with the US team and customers.
You will overlap with a China-based product and engineering org. Some early mornings or late evenings come with the territory; we try to make it fair rather than pretend it is not there.
Small team, wide surface area. You will have more scope than a comparable role at a large company and less scaffolding around you. That trade is the job.
Competitive salary, meaningful equity, and full benefits
Unlimited PTO
A supportive, inclusive, AI-first culture
Direct access to leadership and to customers — short path from idea to shipped
The chance to build the future of AI-driven retail media alongside industry-leading talent
Early-stage venture fund for Generative AI
Embedding VC is an early-stage venture fund that backs Generative AI startups. It provides seed to early-stage capital and operational guidance, leveraging a team of current and former builders and operators who can help portfolio companies grow. The fund’s approach centers on working closely with founders and using hands-on experience to scale AI projects, including those aiming toward AGI, during a period described as a new computing super cycle driven by AI innovations. Unlike funds that rely mainly on financial leverage, Embedding VC differentiates itself through its deep operator network, practical support, and focus on and commitment to building capable, scalable AI businesses. The overall goal is to support ambitious AI founders to advance the development and deployment of AI technologies and capture value in the expanding AI ecosystem.
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