At a Glance
- Tasks: Support ML engineers with large-scale AI workloads and troubleshoot complex systems.
- Company: Join Lightning AI, the creators of PyTorch Lightning, in a dynamic tech environment.
- Benefits: Competitive salary, comprehensive benefits, flexible work, and professional development opportunities.
- Other info: Hybrid work model with at least 2 days in the London office.
- Why this job: Be a key player in shaping the future of AI technology and infrastructure.
- Qualifications: Strong software engineering skills and experience with Kubernetes and ML systems.
The predicted salary is between 75000 - 95000 £ per year.
About Lightning AI
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
We are looking to hire 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.
EMEA Shifts
We are currently hiring for two EMEA shifts (9AM–7PM CET/CEST):
- Sunday–Wednesday
- Saturday–Tuesday or Thursday–Sunday
Location: London office (hybrid). In‑office requirement of at least 2 days per week and occasional team/off‑site events. We are not able to provide visa sponsorship 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
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.
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.
Compensation
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. £75,000 - £95,000 GBP (annual base salary).
Benefits And Perks
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.
Platform Support Engineer (EMEA) employer: Lightning AI
Lightning AI is an exceptional employer that prioritises innovation and collaboration, offering a dynamic work environment in London. With a strong focus on employee growth, we provide comprehensive benefits, including generous paid time off, professional development support, and a flexible work culture that encourages creativity and teamwork. Join us to be part of a diverse team that is shaping the future of AI technology while enjoying a meaningful career with competitive compensation.
StudySmarter Expert Advice🤫
We think this is how you could land Platform Support Engineer (EMEA)
✨Tip Number 1
Network like a pro! Reach out to current employees at Lightning AI on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Platform Support Engineer role.
✨Tip Number 2
Prepare for technical interviews by brushing up on your Kubernetes and ML infrastructure knowledge. We recommend setting up a mini-project to showcase your skills in troubleshooting distributed systems—this will impress the interviewers!
✨Tip Number 3
Showcase your problem-solving skills during interviews. Be ready to discuss past experiences where you diagnosed complex issues, especially in cloud environments. We love hearing about real-world examples!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Lightning AI.
We think you need these skills to ace Platform Support Engineer (EMEA)
Some tips for your application 🫡
Show Your Technical Skills:When writing your application, make sure to highlight your technical expertise, especially in areas like Kubernetes, ML infrastructure, and troubleshooting. We want to see how your experience aligns with the role of a Platform Support Engineer.
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language to describe your past experiences and how they relate to the challenges we face at Lightning AI. We appreciate clarity as much as you do!
Tailor Your Application:Don’t just send a generic application! Tailor it to reflect the specific requirements and responsibilities mentioned in the job description. Show us that you understand what we’re looking for and how you can contribute.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Lightning AI
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of Kubernetes, PyTorch, and GPU orchestration. Be ready to discuss specific challenges you've faced in these areas and how you resolved them. This will show that you're not just familiar with the tech but can also apply it effectively.
✨Prepare for Real-World Scenarios
Think about common issues that arise in ML infrastructure, like performance bottlenecks or distributed training failures. Prepare to walk through how you would diagnose and troubleshoot these problems during the interview. This practical approach will demonstrate your problem-solving skills.
✨Communicate Clearly and Confidently
Since this role involves working closely with ML engineers, practice explaining complex technical concepts in simple terms. Clear communication is key, so consider doing mock interviews with a friend to refine your delivery and ensure you can articulate your thoughts effectively.
✨Show Your Collaborative Spirit
This position is all about partnership and teamwork. Be ready to share examples of how you've successfully collaborated with others in high-pressure situations. Highlight your ability to work in fast-moving environments and how you contribute to a positive team dynamic.
