Remote AI Platform Support Engineer for ML Infra

Lightning AI

Philippines

On-site

PHP 5,643,000 - 8,777,000

Full time

4 days ago

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Benefits offered by this job

Comprehensive Health Coverage

Meaningful Equity (RSUs)

401(k) matching and pension

Unlimited PTO

Company-wide winter break

Parental & Family leave

Professional development allowance

Wellness stipend

Hybrid work model

In-office meals

Job summary

Lightning AI is hiring an AI Platform Support Engineer for the APAC Customer Experience team. The role supports ML engineers with large-scale training and inference workloads across cloud, Kubernetes, and GPU platforms in production.

The position is remote, open to candidates in the Philippines or Singapore, with a Sunday-Wednesday shift from 7:00 AM to 5:00 PM local time (UTC+8). You’ll diagnose failures, guide customers through complex distributed systems, and drive reliability improvements

Qualifications

Strong software engineering and systems troubleshooting background.

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, networking, and platform engineering teams.
Help improve observability, operational visibility, and troubleshooting workflows.
Improve the customer experience through better processes and technical guidance.