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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.

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.

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.

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.

Drives strategic finance, corporate development, and FP&A initiatives, including pricing, unit economics, GPU financing, M&A diligence, investor communications, and capital allocation. Builds and maintains financial models, leads reporting and forecasting cycles, supports annual and long-range planning, evaluates CapEx and infrastructure investments, and delivers executive-level insights on business performance, risks, and opportunities.

Designs and implements microservices-based platform services, RESTful APIs, backend systems, infrastructure automation, and cloud integrations. Responsibilities include architecture planning, monitoring and alerting, production troubleshooting, disaster recovery, performance optimization, code review, mentoring, and participation in technical planning. Requires strong experience with distributed systems, AWS, GCP or Azure, Kubernetes, microservices, programming in Go, Python or Java, infrastructure as code, networking, security, and scalability.