Jobs at Shelf

Work cross-functionally to automate processes, build AI agents and internal tools, design executive dashboards, and own ad-hoc operational projects across Sales, Marketing, Finance, People, and Operations to help the company scale.

Lead enterprise AI use cases from discovery through deployment and measurable business impact. Identify opportunities, build business cases, configure AI data models, agents, copilots, and workflows, manage implementation milestones and risks, validate solution quality, drive customer adoption, and measure business outcomes. Partner with customers, Account Executives, Solutions Architects, Product, and Engineering teams while creating repeatable implementation playbooks.

Own the full enterprise sales cycle for Shelf’s AI platform, including account targeting, outbound pipeline creation, discovery, evaluation, negotiation, and closing. Develop a deep understanding of the ideal customer profile, use AI and data-driven GTM strategies, attend industry events, and build executive relationships. The role also contributes to developing the company’s enterprise go-to-market playbook and requires startup experience selling B2B SaaS.

Lead company-wide operations in partnership with the founders, improving workflows, eliminating bottlenecks, implementing AI-native tools, optimizing vendors and internal systems, and supporting growth. Own operational initiatives across sales, marketing, revenue operations, recruiting, people operations, legal, analytics, pricing, and cross-functional execution. Drive continuous improvement through dashboards, KPIs, experimentation, unit economics, and data-informed decisions.

Own the frontend architecture and delivery of production Next.js applications for enterprise AI products. Build real-time LLM interfaces, semantic search experiences, knowledge graphs, and agentic builders while managing complex state and high-load performance. Maintain component libraries and design systems, ensure accessibility, localization, security, and cross-browser quality, and monitor production performance. Provide technical mentorship, code reviews, and clear product and architecture decisions.

Lead technical discovery, enterprise solution architecture, security and deployment discussions for AI customer opportunities. Design and guide proof-of-value pilots using customer data, define production reference architectures, and support expansion. Partner with sales, engineering, product, and customer success while advising senior technical stakeholders on data platforms, LLMs, RAG, integrations, cloud environments, and enterprise AI strategy.

Build and own business-critical product experiences across frontend and backend systems. Translate ambiguous product goals into practical, scalable solutions, collaborating with product and design. Develop user-facing features, APIs, application logic, and data flows; improve usability, performance, accessibility, reliability, testing, and security. Instrument production systems, use AI tools in daily engineering work, and maintain high ownership from discovery through launch and iteration.

Design, build, and own production backend services, APIs, data flows, and background processing. Translate ambiguous requirements into technical plans, make architecture and data-model decisions, and maintain reliability, observability, performance, and security in production. Collaborate across product, frontend, and platform teams while improving engineering workflows with AI tooling. Contribute to code and design reviews, technical leadership, and enterprise-grade agentic AI systems.