Jobs at Lightning AI

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.

Build and operate production software, APIs, tooling, and automation for large-scale GPU, bare-metal, and HPC infrastructure. Responsibilities include provisioning, configuration, monitoring, lifecycle management, observability, hardware integration, reliability improvements, and infrastructure capacity deployment. The role partners with networking, data center, platform, and infrastructure teams to design scalable systems and define technical direction.

Drive data-informed decisions across Product, Sales, and Engineering by analyzing usage patterns, business performance, and trends. Build data pipelines, write SQL and Python analyses, create Looker and Data Studio dashboards, monitor opportunities and risks, and enable teams to use analytics tools effectively.

This is a general talent community opportunity rather than a specific open position. Lightning AI invites candidates to submit their information for consideration as future roles become available across U.S. and London hubs, with occasional remote opportunities. The company develops tools and infrastructure for building, training, and deploying AI systems and values ownership, urgency, communication, teamwork, continuous improvement, and long-term thinking.

Own strategic channel and technology partner relationships, develop joint business plans, create co-selling motions, identify partner-sourced opportunities, and drive pipeline, revenue, customer adoption, and field engagement. The role coordinates with sales, solutions engineering, marketing, customer success, and partner teams; manages enablement, executive alignment, quarterly reviews, partner workflows, and performance metrics while helping build an early-stage partner ecosystem.

Operate, scale, and optimize distributed storage infrastructure supporting large-scale AI/ML and HPC workloads. Build Python automation, manage Linux bare-metal systems, troubleshoot storage, hardware, networking, and operating system issues, and improve performance, reliability, monitoring, capacity planning, and lifecycle management. Collaborate with infrastructure, networking, platform, and data center teams on storage deployments and scaling strategies.

Build, validate, and operate large-scale bare-metal GPU infrastructure for AI/ML and HPC workloads. Responsibilities include managing Linux systems, image pipelines, test clusters, provisioning, firmware and driver validation, GPU diagnostics, performance analysis with NVIDIA DCGM, automation, virtualization, and hardware management interfaces. The role requires troubleshooting across hardware and software layers while collaborating with infrastructure, hardware, data center, platform, and ML teams.

The Senior People Partner will support engineering and technical leaders on organizational design, leveling, promotions, compensation, employee relations, performance management, and exits. The role will strengthen manager capabilities, improve technical hiring practices, build scalable people systems, and manage global employment complexity. This position requires strong judgment, communication, and influence in a fast-scaling, distributed AI and technology company.

Lead technical discovery, demonstrations, proof-of-concepts, and post-sales engagements for Lightning AI’s machine learning platform. Translate customer requirements into scalable AI solutions, support enterprise evaluations, partner with sales and cross-functional teams, influence product direction, and drive customer adoption. The role also involves coaching technical field leaders, supporting forecasting and deal execution, and contributing to solutions engineering growth in a fast-moving startup.

Operate and scale large GPU infrastructure platforms, including Linux systems, bare-metal environments, provisioning workflows, observability, and reliability automation. Responsibilities include platform deployment, incident response, break/fix operations, customer provisioning, on-call participation, infrastructure troubleshooting, and collaboration across engineering, networking, customer success, and software teams. The role also develops automation to reduce manual work and improve operational efficiency.

Own compensation philosophy, incentive and equity design, compensation planning cycles, market benchmarking, offer and leveling frameworks, and analytical models. Build compensation and equity tools, dashboards, and reporting for pay equity, offer competitiveness, retention risk, and performance cycles. Partner cross-functionally with People, Finance, and senior leaders to translate market and modeling insights into compensation decisions.