Jobs at PointFive

Owner of inbound SMB deals and contributor to enterprise pipeline: prospect named accounts, run discovery with engineering and FinOps buyers, execute account plans, demo, negotiate, close SMB deals, represent the company at events, and maintain CRM and pipeline hygiene.

Build enterprise pipeline by prospecting named accounts through cold calls, email, LinkedIn, and other channels. Partner with enterprise account executives on account plans, stakeholder mapping, discovery calls, pipeline reviews, and deal strategy. Meet targets for qualified meetings and pipeline creation, maintain CRM activity records, support social selling, attend industry events, and develop technical knowledge of cloud infrastructure, FinOps, DevOps, and AI cost management.

Build and own an agentic support automation system that triages, diagnoses, and resolves enterprise customer issues. Define and measure agent quality, response times, resolution times, and CSAT. Handle escalations, improve the system using resolved cases, partner with Engineering and Product on recurring issues, create self-service documentation, and provide support insights for the product roadmap.

Create social-native content across video, carousels, infographics, posts, blogs, and research pages. Grow PointFive’s LinkedIn and X audiences, develop executive and employee voices, translate technical research into compelling stories, and improve organic discovery through search and AI answer engines. Work directly with founders, researchers, engineers, and marketing leadership while analyzing content performance and refining the publishing strategy.

Own full-cycle bookkeeping for PointFive’s Israeli entity and US subsidiary, including monthly close, reconciliations, intercompany activity, payroll, AP, AR, payments, statutory reporting, and tax support. Manage Israeli and US bookkeeping requirements, spend management in Ramp, vendor onboarding, audits, and multi-entity accounting processes. Improve finance automation and workflows in NetSuite and Ramp while supporting external tax and accounting advisors.

Research and build secure runtime infrastructure for AI coding agents across macOS, Windows, and Linux endpoints. Responsibilities include reverse-engineering agent behavior, designing sandboxing and isolation mechanisms, building endpoint harnesses, deploying local LLMs, evaluating inference runtimes, managing model lifecycles, benchmarking performance, and investigating security boundaries between agents, models, shells, and enterprise resources.

Research and ship Kubernetes cost-efficiency detections using production workload, billing, metrics, and inventory data. Own the Kubernetes detection roadmap, validate recommendations against customer environments, translate findings into engineering requirements, and coordinate delivery to production. Support pre-sales and post-sales engagements, establish Kubernetes efficiency best practices, and publish technical thought leadership. The role requires hands-on production Kubernetes, AWS, quantitative modeling, and strong cross-functional communication.

Own the strategy and roadmap for TokenShift, a product that identifies and reduces wasted AI token spend. Translate technical knowledge of AI agents, LLM workloads, inference, orchestration, and observability into product requirements. Partner with R&D, design, data science, customers, and go-to-market teams to ship features, define metrics, improve cost visibility, and drive adoption. The role requires technical product management experience with complex B2B infrastructure or developer products.

Build and maintain TokenShift’s endpoint infrastructure, including CLI installation and enrollment, MDM packaging, telemetry, agent harnesses, compression rules, and proxy reliability. Develop platform-specific support across Windows, macOS, and Linux while integrating AI coding agents such as Claude Code, Cursor, Copilot CLI, and Codex. Ensure installations, hooks, compression, and command routing are resilient, transparent, and scalable across developer fleets.

Lead and own backend and AI technical initiatives end-to-end: design scalable AI/data infrastructure, build cloud production systems, drive architecture and code quality, collaborate with product and data teams, and mentor engineers.

Lead high-impact backend and AI infrastructure initiatives end-to-end: design scalable cloud data pipelines, integrate LLM/AI workflows, drive architecture and engineering standards, mentor engineers, and align product and technical stakeholders.