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Lightning AI is hiring an AI Platform Support Engineer to join the APAC Customer Experience team. This is a technical support role focused on helping ML engineers troubleshoot and optimize large-scale training and inference workloads running on cloud infrastructure, Kubernetes, and GPU platforms.
You will work directly with customer engineering teams to diagnose and resolve complex distributed systems and ML infrastructure issues. This is not a ticket-routing role—you'll be a technical partner helping customers understand and fix problems ranging from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability.
Key responsibilities include:
- Partner directly with customers running production training and inference workloads
- 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
- Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage
- Troubleshoot PyTorch, CUDA, NCCL, and inference serving issues
- Analyze logs, metrics, traces, and system behavior to isolate root causes
- Debug containerized workloads running across Kubernetes and bare metal GPU environments
- Build credibility with customers through strong technical reasoning and clear communication
Lightning AI is the company behind PyTorch Lightning, an end-to-end platform for developing, training, and deploying AI systems. The company merged with Voltage Park (a neocloud and AI Factory) to combine developer-first software with cost-efficient, large-scale compute. Lightning AI is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute, with offices in NYC, San Francisco, Seattle, and London.
This role is remote and open to candidates based in the Philippines. The position operates on a Sunday-Wednesday shift schedule, 7:00 AM to 5:00 PM local time (UTC+8).
