AI Platform Support Engineer (US)

Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.   What We’re Looking For Lightning AI is looking to hire an AI Platform Support Engineer  to join our US 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 sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You’ll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are 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. 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 To set clear expectations: 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. What You’ll Need Required Qualifications Infrastructure & Systems 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 ML Infrastructure Experience 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 Collaboration 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 Ideal Experience 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 This role is hybrid out of our Seattle or San Francisco offices, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday–Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at thi