AI Platform Support Engineer (EMEA)
£75k - £95k • London, England, United Kingdom • Full-time
Posted 5mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
London, England, United Kingdom
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Infrastructure Engineer
About the job
Lightning AI is seeking AI Platform Support Engineers to join their EMEA Customer Experience team in London. This role focuses on supporting ML engineers who manage large-scale AI training and inference workloads on cloud infrastructure, Kubernetes, and GPU platforms. You will act as a technical partner, diagnosing failures, enhancing reliability, and guiding customers through complex distributed systems challenges. The work involves a wide range of issues, from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, and networking bottlenecks. You will gain exposure to diverse AI workloads across industries and contribute to shaping the infrastructure for next-generation ML applications.
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.
- 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.
Requirements
- 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.
- 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.
- Strong communication skills.