Jobs at Lightning AI

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, build, and maintain scalable backend services and APIs powering Lightning AI Studio. Develop platform capabilities for authentication, resource management, multi-tenancy, and developer workflows. Improve reliability, observability, scalability, and performance across cloud-native infrastructure. Collaborate with product, infrastructure, and AI engineering teams, lead technical initiatives from architecture through production, evaluate engineering processes, and mentor engineers.

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.

Partners with senior business leaders on organizational design, team structure, leveling, promotions, compensation, employee relations, performance management, and exits. Builds manager capability, improves hiring quality, and develops scalable people processes with Legal, Total Rewards, and Finance. Supports globally distributed teams and helps reduce organizational risk through practical, high-impact systems and policies.

Own the company’s data infrastructure, including ETL/ELT pipelines, clean analytical tables, data quality, schema management, freshness monitoring, lineage, and alerting. Partner with engineering, product, ML, data science, and go-to-market teams to improve instrumentation and deliver reliable product and operational insights. This high-scope role requires hands-on technical leadership, data governance, and scaling analytics capabilities in a rapidly changing environment.

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 deploy production AI systems for customers, translating business objectives into scalable technical solutions. Responsibilities include architecture, proof-of-concepts, software development, deployment, monitoring, debugging, inference optimization, and distributed systems operation. The engineer partners with customer engineering teams, collaborates with product and engineering, improves reusable platform capabilities, and owns technical engagements from discovery through production scaling.

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.

Leads Lightning AI’s global customer experience organization, including Technical Account Managers and Support Engineers. Owns strategy, onboarding, adoption, technical success, retention, expansion, account health, escalations, and customer experience metrics. Partners closely with Product, Engineering, Infrastructure, Sales, and Finance to resolve customer issues, influence product priorities, and scale high-touch support through automation and self-service. The role requires strong technical fluency, executive communication, operational rigor, and experience supporting complex SaaS, cloud, developer platform, or AI infrastructure products.

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.

Design, optimize, and deploy large language model training and post-training pipelines. Improve model quality through fine-tuning, reinforcement learning, preference optimization, evaluation, and experimentation. Build PyTorch-based infrastructure, optimize distributed multi-GPU training, diagnose performance and convergence issues, and develop production-ready AI systems. Collaborate with researchers, infrastructure engineers, platform teams, and customers while contributing to open-source projects and reusable training capabilities.

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 Revenue Operations Manager will lead initiatives across GTM systems, reporting, forecasting, pipeline management, territory design, compensation, and process optimization. They will manage and improve the SaaS technology stack, implement scalable AI-enabled systems and processes, and partner with Sales, Marketing, Customer Success, Product, Finance, Legal, and executive leadership to drive alignment and growth.

Build and administer AI-enabled People Operations workflows, systems, integrations, dashboards, and secure employee-data solutions. Manage employee lifecycle administration, HRIS updates, payroll and benefits support, audits, documentation, and employee or manager inquiries. Partner cross-functionally with Finance, Legal, and IT to automate manual processes, maintain compliant systems, and deliver internal solutions from design through production.

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.