At a Glance
- Tasks: Support ML engineers with large-scale training and inference workloads across cloud infrastructure.
- Company: Join Lightning AI, the creators of PyTorch Lightning, in a dynamic tech environment.
- Benefits: Competitive salary, equity options, comprehensive health coverage, and generous paid time off.
- Other info: Hybrid role in London with excellent career growth opportunities.
- Why this job: Be a technical partner solving complex problems in cutting-edge AI systems.
- Qualifications: Strong software engineering skills and experience with Kubernetes and ML infrastructure.
The predicted salary is between 75000 - 95000 € per year.
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 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 two EMEA shifts (9AM–7PM CET/CEST):
- Sunday–Wednesday
- Saturday–Tuesday OR Thursday–Sunday
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
- 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. The anticipated annual base salary range for this role is: £75,000 - £95,000 GBP.
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.
Platform Support Engineer (EMEA) in London employer: Lightning AI
Lightning AI is an exceptional employer that prioritises employee growth and well-being, offering a hybrid work environment in the vibrant city of London. With a strong focus on collaboration and innovation, employees are empowered to tackle complex technical challenges while receiving comprehensive benefits, including generous paid time off and professional development support. The inclusive work culture fosters diversity and encourages team members to thrive, making it an ideal place for those seeking meaningful and rewarding careers in AI infrastructure.
StudySmarter Expert Advice🤫
We think this is how you could land Platform Support Engineer (EMEA) in London
✨Tip Number 1
Get to know the company inside out! Research Lightning AI's products, values, and recent news. This will help you tailor your conversations and show that you're genuinely interested in being part of the team.
✨Tip Number 2
Network like a pro! Connect with current employees on LinkedIn or attend industry events. Building relationships can give you insider info and might even lead to a referral—definitely a win!
✨Tip Number 3
Prepare for technical interviews by brushing up on your troubleshooting skills. Practice explaining complex problems clearly and concisely, as you'll need to demonstrate your ability to communicate effectively with ML engineers.
✨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 serious about joining the Lightning AI team.
We think you need these skills to ace Platform Support Engineer (EMEA) in London
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 skills align 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 experiences and how they relate to the challenges we face at Lightning AI. We appreciate direct communication!
Tailor Your Application:Don’t just send a generic application! Tailor it to reflect your understanding of our company and the specific role. Mention how you can contribute to our mission of supporting ML engineers and improving platform reliability.
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 shows you’re serious about joining our team!
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 role is all about technical problem-solving, so showing your expertise will definitely impress.
✨Prepare for Real-World Scenarios
Think about common issues that arise in ML infrastructure and be prepared to walk through how you would troubleshoot them. For example, consider how you'd handle a distributed training failure or a performance bottleneck. Practising these scenarios can help you articulate your thought process during the interview.
✨Show Off Your Communication Skills
Since you'll be working closely with ML engineers, it's crucial to demonstrate your ability to communicate complex technical concepts clearly. Practice explaining difficult topics in simple terms, as this will show that you can be an effective partner to customer engineering teams.
✨Ask Insightful Questions
Prepare some thoughtful questions about Lightning AI's platform and the challenges they face. This not only shows your interest in the role but also gives you a chance to assess if the company aligns with your career goals. Questions about their approach to reliability improvements or tooling development can spark great conversations.
