Top Tech Jobs & Startup Jobs

Supports the CEO of a fast-growing AI health technology company by managing complex calendars, meeting priorities, travel, conferences, offsites, correspondence, inbox organization, records, and confidential information. The role also provides light office support and assists with evolving operational projects. Success requires exceptional organization, communication, discretion, judgment, initiative, and proficiency with modern productivity tools in a fast-paced hybrid startup environment.

Own core product areas from customer discovery through launch and commercial scale. Develop product vision, strategy, roadmaps, specifications, prototypes, and lifecycle plans. Partner closely with engineering, design, sales, growth, finance, and customer teams to build AI-powered healthcare products. Monitor customers, workflows, competitors, market shifts, and regulatory changes to guide product decisions.

Own frontline customer product support, troubleshoot and triage issues, identify recurring problems, and coordinate resolutions with Product and Engineering. Build help articles, FAQs, training materials, videos, and SOPs; enable internal BizOps and Sales teams; analyze support trends; represent customer feedback in product discussions; and develop expertise in healthcare technology, AI, integrations, and workflows.

Own the company’s 0-to-1 growth marketing engine, including pipeline planning, integrated campaigns, ABM, paid acquisition, conversion optimization, lifecycle marketing, HubSpot automation, experimentation, and reporting. Partner closely with Sales, RevOps, and Content to build scalable campaigns, improve lead management, and drive measurable pipeline and revenue in an early-stage B2B SaaS environment.

Build and own the systems, data, processes, and infrastructure powering the company’s go-to-market engine. Responsibilities include CRM ownership, lifecycle and deal-stage definition, outbound optimization, ICP scoring, pipeline generation, forecasting, dashboard development, and identifying improvements across the full revenue funnel. Partner with Sales, Growth, Marketing, Business Operations, and leadership in a high-growth startup environment.

Own the full sales cycle for exacare ai’s post-acute care platform, including prospecting, discovery, demos, pricing, negotiation, and closing. Build pipeline within nursing home and post-acute markets, develop relationships with senior decision-makers, collaborate with the CEO and Customer Success, refine go-to-market strategy, and maintain accurate CRM forecasting. The role requires outbound sales strength, strong communication, business curiosity, coachability, grit, and independent ownership.

Build and scale platform infrastructure, backend services, cloud systems, CI/CD pipelines, and internal developer tooling. Own highly available production systems using TypeScript, PostgreSQL, AWS, Docker, and Kubernetes. Improve deployment workflows, observability, incident response, infrastructure automation, security, and system resiliency. Design APIs, event-driven workflows, queues, and integrations while partnering with product, engineering, and operations teams on scalable healthcare technology.

Develop and scale customer-facing analytics, dashboards, reports, and data products for a healthcare AI platform. Partner with Product, Engineering, Sales, BizOps, customers, and other stakeholders to translate needs into data requirements and actionable insights. Lead reporting initiatives, resolve customer reporting issues, support high-stakes customer interactions, improve internal workflows, and help productize operational and clinical data into differentiated customer value.

Build and scale frontend architecture, responsive data-rich experiences, shared components, design-system standards, accessibility practices, Storybook documentation, automated quality guardrails, and frontend tooling. Contribute to backend APIs and platform improvements while collaborating with product and design teams. Mentor engineers and improve frontend quality, consistency, maintainability, and scalability across the organization.

Build and scale production machine learning infrastructure, including data and training pipelines, deployment workflows, monitoring, and supporting systems. Partner with researchers and ML practitioners to productionize models, improve reliability, scalability, observability, reproducibility, speed, and cost efficiency, and automate operational processes across the ML lifecycle.