Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("Company in London, United Kingdom"), this listing's salary midpoint is about 59% lower. The offer sits below the benchmark range (€11,936–€22,426). The listed pay band (€6,250–€7,917) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 1 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| All roles in London, United Kingdom | €4,030/per month | €9,310/per month | €15,737/per month |
| Company in London, United Kingdom | €11,936/per month | €17,181/per month | €22,426/per month |
Lightning AI is hiring an AI Platform Support Engineer for the EMEA Customer Experience team. The role supports 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 two EMEA shifts (9AM–7PM CET/CEST). 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.
Job Details
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
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 and ability to work directly with highly technical customers and engineering teams
Skills & Technologies
Benefits & Perks
Recruitment Process
- 1Screening
- 2Technical interview
- 3Culture fit interview
- 4Offer
| Location | Active listings |
|---|---|
| Remote - Global | 1 |
| San Francisco, United States | 1 |
| London, United Kingdom | 1 |
| New York, United States | 1 |
| Seattle, United States | 1 |
| Role type | Active listings |
|---|---|
| Research Engineer | 1 |
| Role level | Active listings |
|---|---|
| Mid-Level | 1 |
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