Platform Support Engineer

Who We AreLightning 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 ForWe’re looking for engineers who understand the realities of running machine learning workloads at scale.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 DoWork Directly With ML EngineersPartner directly with customer engineering teams running training and inference workloads in productionHelp customers diagnose and resolve complex distributed systems and ML infrastructure issuesAct as a technical advisor during high impact incidents and platform degradation eventsTranslate infrastructure level issues into actionable guidance for ML engineersBuild credibility with customers through strong technical reasoning and clear communicationDebug ML Infrastructure & Distributed WorkloadsInvestigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systemsTroubleshoot PyTorch, CUDA, NCCL, and inference serving related issuesAnalyze logs, metrics, traces, and system behavior to isolate root causesDebug containerized workloads running across Kubernetes and bare metal GPU environmentsSupport customers scaling workloads across multi node GPU systemsDiagnose performance bottlenecks involving compute, memory, networking, or storageImprove Reliability & Platform OperationsIdentify recurring patterns across customer issues and drive long term reliability improvementsContribute to post incident reviews and operational improvementsBuild internal tooling, automation, documentation, and runbooksPartner closely with infrastructure, networking, and platform engineering teamsHelp improve observability, operational visibility, and troubleshooting workflowsImprove the customer experience through better processes and technical guidanceWhat This Role Is NotTo set clear expectations:This is not a traditional help desk or ticket routing support roleThis is not purely customer success or account managementThis is not a backend engineering roleThis is not a passive escalation positionThis role is for engineers who enjoy solving difficult technical problems while working closely with other engineers.What You’ll NeedRequired QualificationsInfrastructure & SystemsStrong software engineering and systems troubleshooting backgroundExperience with Kubernetes and containerized environmentsLinux systems knowledge, including networking, storage, process management, and performance tuningExperience with cloud infrastructure and distributed systemsExperience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetryML Infrastructure ExperienceHands on experience operating machine learning workloads in production or research environmentsExperience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCLFamiliarity with GPU infrastructure and orchestrationExperience troubleshooting performance, reliability, or scaling issues in ML infrastructureUnderstanding of the operational challenges involved in running ML systems at scaleCollaborationStrong communication skills and ability to work directly with highly technical customers and engineering teamsComfortable operating in fast moving, highly ambiguous environmentsEnjoys solving complex technical problems collaborativelyNice-to-HavesExperience with large scale model training or distributed inference systemsFamiliarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platformsExperience with InfiniBand, RDMA, or high-performance networkingExperience operating bare metal infrastructureFamiliarity with storage systems commonly used in ML environmentsExperience working at an AI infrastructure, cloud, MLOps, or developer tooling companyContributions to platform engineering, developer infrastructure, or operational tooling projectsExperience writing automation, tooling, or scripts in Python or similar languagesThis 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 this time. We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits.The anticipated annual base salary range for this role is:$115,000 - $140,000 USDBenefits And PerksWe offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success. Benefits may vary by location, team, and role.Benefits IncludeComprehensive 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 holidaysPaid parental leaveProfessional development supportWellness and work-from-home stipendsFlexible work environmentAt Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.