Lightning AI

Role Description Lightning AI is seeking an experienced Infrastructure Operations Engineer to help scale and operate our next-generation AI infrastructure platform. Our InfraOps team sits at the center of reliability, automation, and operational scale for GPU infrastructure. This team owns break/fix operations, incident response, customer provisioning, observability, and the automation systems that keep complex infrastructure running efficiently. In this role, you’ll work hands-on with large-scale GPU environments, Linux systems, bare metal infrastructure, provisioning workflows, and platform reliability. You’ll partner closely with Infrastructure Engineering, Network Operations, and Software Platform teams to troubleshoot issues, improve operational efficiency, and build automation that reduces manual toil over time. We’re flexible on location for this team. This role can work hybrid out of one of our US-based hubs (Seattle, NYC, or SF) or fully remote within the U.S., with occasional company and team offsites. We are not able to provide visa sponsorship for this position at this time. Qualifications - 8+ years working with Linux as a server/hosting platform, extra points for Ubuntu experience. - 5+ years experience with AWS. - 2+ years experience with Kubernetes and strong container fundamentals. - 2+ years experience with Terraform and Ansible. - 2+ years with network attached storage management (via NFS, ceph, or other protocols). Extra points for experience with VAST storage systems. - Experience with monitoring systems (Prometheus, ELK stack). - Familiarity with the gitops workflow. - Software development experience using Python, Go, bash, or other languages for the purposes of automation & connecting systems & APIs together. - Deep networking fundamentals, extra points for experience with datacenter level networks, 400Gb ethernet, and Infiniband. - Experience building and delivering complex systems. - Effective at navigating tradeoffs between design, risk, cost, and outcomes. - Comfortable with navigating ambiguity. - Strong written and oral communication. Requirements - Experience with bare metal hardware troubleshooting and provisioning, extra points for working with Dell hardware. - Experience with GPU servers, both in bare metal form or under virtualization. - Deep experience with network switches, routers, and firewalls, particularly SONiC switches, Palo Alto firewalls and Juniper Networks as vendors. - Experience with VAST storage systems. Benefits - Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.) - Retirement and financial wellness support (U.S.); Pension contribution (U.K.) - Generous paid time off, plus holidays - Paid parental leave - Professional development support - Wellness and work-from-home stipends - Flexible work environment

Role Description Lightning AI is looking to hire a Platform Support Engineer to join our APAC Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments. This role is not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems. The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You’ll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications. This role is remote and open to candidates based in either the Philippines or Singapore. The role follows a Thursday–Sunday schedule, with working hours from 7:00 AM to 5:00 PM local time (UTC+8). What You'll Do - Work Directly With ML Engineers - Partner directly with customer engineering teams running training and inference workloads in production - Help customers diagnose and resolve complex distributed systems and ML infrastructure issues - Act as a technical advisor during high impact incidents and platform degradation events - Translate infrastructure level issues into actionable guidance for ML engineers - Build credibility with customers through strong technical reasoning and clear communication - Debug ML Infrastructure & Distributed Workloads - Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems - Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues - Analyze logs, metrics, traces, and system behavior to isolate root causes - Debug containerized workloads running across Kubernetes and bare metal GPU environments - Support customers scaling workloads across multi node GPU systems - Diagnose performance bottlenecks involving compute, memory, networking, or storage - Improve Reliability & Platform Operations - Identify recurring patterns across customer issues and drive long term reliability improvements - Contribute to post incident reviews and operational improvements - Build internal tooling, automation, documentation, and runbooks - Partner closely with infrastructure, networking, and platform engineering teams - Help improve observability, operational visibility, and troubleshooting workflows - Improve the customer experience through better processes and technical guidance What This Role Is Not - This is not a traditional help desk or ticket routing support role - This is not purely customer success or account management - This is not a backend engineering role - This is not a passive escalation position - This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. Qualifications - Strong software engineering and systems troubleshooting background - Experience with Kubernetes and containerized environments - Linux systems knowledge, including networking, storage, process management, and performance tuning - Experience with cloud infrastructure and distributed systems - Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry - Hands on experience operating machine learning workloads in production or research environments - Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL - Familiarity with GPU infrastructure and orchestration - Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure - Understanding of the operational challenges involved in running ML systems at scale - Strong communication skills and ability to work directly with highly technical customers and engineering teams - Comfortable operating in fast moving, highly ambiguous environments - Enjoys solving complex technical problems collaboratively Nice-to-Haves - Experience with large scale model training or distributed inference systems - Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms - Experience with InfiniBand, RDMA, or high-performance networking - Experience operating bare metal infrastructure - Familiarity with storage systems commonly used in ML environments - Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company - Contributions to platform engineering, developer infrastructure, or operational tooling projects - Experience writing automation, tooling, or scripts in Python or similar languages Benefits - Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.) - Retirement and financial wellness support (U.S.); Pension contribution (U.K.) - Generous paid time off, plus holidays - Paid parental leave - Professional development support - Wellness and work-from-home stipends - Flexible work environment