AI Platform Support Engineer (EMEA)

原始資料於 2026-11-04T15:07:56.209314+00:00 查閱；內容可能已更新，請查看原始職位。

What We’re Looking For
Lightning AI is looking to hire AI Platform Support Engineers to join our EMEA 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.
We are currently hiring for the following shift (9AM–7PM CET/CEST):
Wednesday–Saturday
This role is hybrid out of our London office, with an in-office requirement of at least 2 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time.
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
Nice-to-Haves
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