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 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 reviews, mentoring, and sprint planning. The role requires extensive experience with backend and distributed systems, cloud platforms, Kubernetes, microservices, programming languages, infrastructure as code, networking, security, and scalability.

Own and scale Lightning AI’s global corporate tax function across a multi-entity organization. Lead federal, state, local, indirect, and property tax compliance; ASC 740 tax accounting; forecasting; audit support; M&A tax; risk management; and process automation. Build policies, controls, documentation, and a scalable tax operating roadmap while partnering with Finance, Accounting, FP&A, Legal, auditors, and external advisors.

Support multi-entity general ledger close activities, including journal entries, reconciliations, intercompany, payroll accruals, prepaids, fixed assets, leases, financial statements, and flux analysis. Help transform and document the close function through NetSuite implementation, process redesign, and control development. Support external audits and maintain accurate, auditable accounting records.

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.

Support ML engineering teams running production training and inference workloads across Kubernetes, cloud, Linux, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through tooling, automation, documentation, runbooks, and operational processes. The role is hybrid in London, requires at least two office days weekly, and supports EMEA shifts.

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.

Designs, implements, and maintains secure network infrastructure for Lightning AI’s production and AI environments. Responsibilities include firewall, VPN, IDS/IPS, segmentation, vulnerability assessment, penetration testing, SIEM monitoring, incident response, compliance documentation, access controls, and security automation. The role partners with infrastructure and engineering teams to mitigate threats and optimize security controls.

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.