Top Tech Jobs & Startup Jobs in NYC, NY

The Enterprise Account Executive will sell technical, developer-focused AI and machine learning products to enterprise technical audiences. Responsibilities include outbound prospecting, lead qualification, product demonstrations, contract negotiation, closing deals, pipeline management, revenue target attainment, customer discovery, and cross-functional collaboration with product, engineering, and marketing teams. The role also requires maintaining accurate sales data in HubSpot and providing customer insights to inform product development.

Lead and scale an enterprise Account Executive team selling AI infrastructure, developer platforms, compute, and related solutions. Own pipeline generation, forecasting, deal inspection, quota attainment, territory planning, and enterprise sales methodology. Coach sellers through complex technical sales involving ML, infrastructure, engineering, procurement, finance, and executive stakeholders. Build repeatable go-to-market processes, partner cross-functionally, shape product positioning, recruit talent, and develop the enterprise sales playbook.

Own enterprise product strategy and customer outcomes for Lightning AI's AI infrastructure platform. Identify high-value opportunities, translate technical user needs into product requirements, remove adoption blockers, and partner with Engineering, Sales, Customer Success, Marketing, and Design. Drive integrations, APIs, workflows, instrumentation, and enterprise expansion while coaching engineering teams and delivering intuitive products for developers.

Own customer health, adoption, retention, renewal readiness, expansion influence, and executive relationships. Build scalable customer success processes covering onboarding, success planning, business reviews, health scoring, forecasting, escalations, and expansion handoffs. Partner with Sales, Product, Engineering, Support, and other teams to improve customer outcomes, develop playbooks, provide roadmap feedback, and establish a proactive, technically informed customer success culture.

Own the vision, roadmap, and customer experience for a GPU cloud infrastructure platform. Define provisioning, capacity, scheduling, networking, storage, workload execution, APIs, pricing, and consumption models. Partner with engineering, operations, sales, finance, customers, and executives to improve reliability, utilization, performance, and adoption. Use product and infrastructure data to guide tradeoffs and lead products from discovery through launch and iteration.

Build and operate backend services, control planes, and automation for Lightning AI’s managed Kubernetes and Slurm infrastructure. Develop distributed systems for cluster provisioning, lifecycle management, scheduling, observability, reliability, and scalability across large-scale GPU environments. Diagnose complex production issues and collaborate with infrastructure, AI, and platform engineering teams. Contribute to architecture, technical design, mentoring, engineering practices, and on-call operations.

Designs, deploys, automates, and operates large-scale NVIDIA InfiniBand fabrics for GPU clusters. Manages UFM, switches, firmware, congestion control, routing, QoS, observability, troubleshooting, capacity planning, and incident response. Collaborates with AI platform, GPU infrastructure, storage, and systems teams to build reliable AI Factory environments.

Design end-to-end user experiences for AI and machine learning products, collaborating with product and engineering teams from strategy through release. Create user journeys, interaction designs, prototypes, and visual designs; conduct user research; engage with open-source AI and enterprise users; identify opportunities to reduce friction; and advocate for scalable design systems. This hybrid role requires strong product, interaction, and visual design skills in a fast-paced startup environment.

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.

Develop and scale Lightning AI’s backend platform using Go, spanning APIs, infrastructure, billing, security, and integrations. Own features end to end, design reliable and scalable systems, improve architecture and performance, maintain software quality and continuous delivery, reduce technical debt, and mentor engineers. Collaborate with engineering, product, and design teams in a fast-changing SaaS environment.