AI Platform Support Engineer (US)
$115k - $140k • New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States
Posted 1mo ago
Job Location
New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States
Tech Stack
Remote Work Policy
On-site
Categories
AI Engineer
About the job
Lightning AI is seeking an AI Platform Support Engineer to join their US Customer Experience team. This role is designed for individuals who are technical partners to ML teams, helping them diagnose and resolve complex issues related to large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms. You will be instrumental in supporting engineers as they train models, deploy inference systems, and scale GPU workloads in production, tackling problems ranging from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking, and storage performance. This position offers exposure to diverse AI workloads and the opportunity to shape the infrastructure powering future ML applications.
Responsibilities
- 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.
- 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.
- 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 and networking teams.
Requirements
- Experience supporting ML engineers running large-scale training and inference workloads.
- Familiarity with cloud infrastructure, Kubernetes, and GPU platforms in production environments.
- Ability to diagnose and resolve complex distributed systems and ML infrastructure issues.
- Experience troubleshooting issues related to distributed training, Kubernetes orchestration, GPU allocation, networking, and storage.
- Proficiency in troubleshooting PyTorch, CUDA, NCCL, and inference serving.
- Experience analyzing logs, metrics, traces, and system behavior.
- Ability to debug containerized workloads on Kubernetes and bare metal GPU environments.
- Experience supporting customers scaling workloads across multi-node GPU systems.
- Skill in diagnosing performance bottlenecks.
- Strong technical reasoning and clear communication skills.