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.

The Way We Work

The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:

Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.
Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.
Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.
Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.
Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.
Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.

What We’re Looking For

Lightning AI is looking to hire an AI 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 the Philippines or Singapore. We are hiring for a variety of shift schedules, 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