AI Platform Support Engineer (US)

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 an AI 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.

The anticipated annual base salary range for this role is:

$115,000—$140,000 USD

BENEFITS AND PERKS

We offer a comprehensive and competitive benefits package designed to support

our employees’ health, well-being, and long-term success.

Benefits

may vary by

location, team, and role.

Benefits

include:

* Comprehensive medical, dental and vision coverage (U.S.); Private medical and

dental insurance (U.K.)

* Retirement and financial wellness support (U.S.); Pension contribution (U.K.)

* Generous paid time off, plus holidays

* Paid parental leave

* Professional development support

* Wellness and work-from-home stipends

* Flexible work environment

At Lightning AI, we are committed to fostering an inclusive and diverse

workplace. We believe that diverse teams drive innovation and create better

products. We provide equal employment opportunities to all employees and

applicants without regard to race, color, religion, gender, sexual orientation,

gender identity, national origin, age, disability, veteran status, or any other

protected characteristic. We are dedicated to building a culture where everyone

can thrive and contribute to their fullest potential.