Platform Support Engineer (US)

Job Application for Platform Support Engineer (US) at Lightning AI

Platform Support Engineer (US) San Francisco, California, United States; Seattle, Washington, United States

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.

What We’re Looking For

Lightning AI is looking to hire a Platform Support Engineer to join our US 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 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 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

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

What This Role Is Not

To set clear expectations:

This is not a traditional help desk or ticket routing support role

This is not purely customer success or account management

This is not a backend engineering role

This is not a passive escalation position

This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers.

What You’ll Need

Required Qualifications

Infrastructure & Systems

Strong software engineering and systems troubleshooting background

Experience with Kubernetes and containerized environments

Linux systems knowledge, including networking, storage, process management, and performance tuning

Experience with cloud infrastructure and distributed systems

Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry

ML Infrastructure Experience

Hands on experience operating machine learning workloads in production or research environments

Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL

Familiarity with GPU infrastructure and orchestration

Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure

Understanding of the operational challenges involved in running ML systems at scale

Collaboration

Strong communication skills and ability to work directly with highly technical customers and engineering teams

Comfortable operating in fast moving, highly ambiguous environments

Enjoys solving complex technical problems collaboratively

Ideal Experience

Experience with large scale model training or distributed inference systems

Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms

Experience with InfiniBand, RDMA, or high-performance networking

Experience operating bare metal infrastructure

Familiarity with storage systems commonly used in ML environments

Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company

Contributions to platform engineering, developer infrastructure, or operational tooling projects

Experience writing automation, tooling, or scripts in Python or similar languages

This 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.…