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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.

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.