Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead and own Pallet's pipeline by building full-channel growth: paid media, OOH, field/events, email/lifecycle, SEO and affiliates. Design experiments, build tracking and attribution, operationalize scalable channels, and partner cross-functionally with marketing, sales, product, and engineering to drive measurable pipeline and revenue outcomes.

Lead full-stack growth for Pallet across outbound, website conversion, SEO, paid, affiliates, and new channels. Develop strategy, run and analyze high-volume experiments, build tracking and attribution, operationalize validated channels, and lead cross-functional projects to scale predictable, automated acquisition.

Build and own Pallet's go-to-market and product marketing strategy: craft launch narratives, create sales enablement, develop market positioning and case studies, coordinate cross-functional launches, and analyze sales and product data to improve messaging and GTM impact.

Manage day-to-day operations for office spaces, oversee logistics for office expansion, coordinate events, and ensure a welcoming environment.

Build Pallet's first Revenue Operations function from scratch: design processes, reporting, forecasting, pipeline hygiene, CRM discipline, lead routing and territory frameworks, commission administration, and cross-functional reporting to enable scalable, repeatable revenue growth.

The Deployment Strategist manages enterprise customer deployments, ensuring successful integration of AI workflows, and serves as the main client contact throughout the process.

The Product Engineer will customize solutions for clients, build AI-driven logistics products, collaborate with teams, and influence product direction from ideation to deployment.

Deploy and operate production AI agents for logistics customers: reverse-engineer legacy systems, build integrations, debug distributed production failures, run onsite go-lives (~25% travel), and convert recurring implementation patterns into product capabilities to influence roadmap.

Deploy and own production AI agent integrations for large logistics customers: reverse-engineer undocumented systems, build integrations, debug distributed production failures, run onsite launches, and turn recurring patterns into reusable product capabilities while partnering with engineering and product teams.

Build and maintain core platform services that power AI systems and developer workflows. Design and implement authentication, authorization, secrets management, multi-region infrastructure, observability, and operational tooling. Partner with AI and product teams, identify and eliminate systemic risks, and own projects from architecture through production rollout.