Jobs at Hilbert's AI

The AI Growth Architect will tackle growth challenges in the fashion industry, translating insights into actionable strategies and directly impacting customer growth using Hilbert's AI capabilities.

As the first GTM in France, you will be responsible for owning revenue, prospecting and closing enterprise deals, building a team, and defining market strategies. You'll utilize growth engineering principles and engage with executive-level clients to drive business expansion.

Lead Hilbert's content strategy, managing editorial voice, video programming, sales enablement content, and customer stories to position Hilbert as a leader in B2C growth intelligence.

Build and deploy hybrid data integrations for enterprise customers: implement ClickHouse or warehouse-native pipelines (Snowpark/BigQuery), construct AI semantic layers and canonical growth models, partner with AI discovery agents to accelerate onboarding, and lead technical enterprise deployments ensuring performance and reasoning consistency.

Build and own end-to-end product surfaces for an AI agent: frontend React dashboards, backend agentic query engines, enterprise-ready services, admin tools, and integrations. Collaborate with Product and ML teams to design, ship, and operate scalable, secure, configurable systems.

Lead and build Hilbert's end-to-end privacy program embedded in product and engineering; ensure compliance with US, UK, and EU privacy laws; negotiate enterprise DPAs and security addenda; advise on privacy-by-design for agentic AI features; manage incident response and customer legal/security engagement.

Lead enterprise sales into US grocery, QSR, retail, and consumer accounts: source pipeline, open doors, close complex data/AI deals, build repeatable GTM playbooks, feed product signal, mentor GTM talent, and partner with founders to scale revenue operations.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

First marketing hire responsible for positioning, messaging, GTM strategy, product launches, content (podcast, thought leadership, case studies), events, branding, measurement, and building a marketing team and repeatable systems working closely with founders and GTM leaders.

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.

Act as a founding growth operator using Hilbert to solve live B2C growth problems, identify model failure modes, translate insights into product improvements, partner with sales to convert outputs into growth strategies, and represent the product in high-stakes GTM conversations.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.

Early-career seller who will learn Hilbert’s product in San Francisco (~3 months) then deploy internationally (6–12 months) to build and own a market: generate enterprise pipeline from scratch, close first strategic accounts, form local partnerships, map buyers/competition, and feed market insights back to HQ to establish a permanent regional presence.

Own regional enterprise revenue end-to-end: close large strategic accounts, build and lead a regional sales team, develop repeatable GTM playbooks, shift pipeline to inbound, forecast and scale revenue operations, and partner with founders on GTM strategy.

Own the product vision and roadmap for AI-driven growth products. Engage customers to gather feedback, define requirements, and prioritize features with engineering. Serve as a trusted advisor to drive adoption and measurable customer growth. Collaborate across data, engineering, and business teams to execute in a fast-paced, distributed environment.