Platform Support Engineer
About The Position
Lightning AI is seeking engineers who understand the challenges of running machine learning workloads at scale. This role is a blend of ML systems, cloud infrastructure, Kubernetes, and customer interaction. The successful candidate will support engineers with model training, inference system deployment, and scaling GPU workloads in production. This is not a traditional support role; it involves being a technical partner to ML teams, helping them diagnose failures, improve reliability, and navigate complex distributed systems issues. Problems can range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. The role offers exposure to diverse AI workloads and the opportunity to shape the infrastructure for future ML applications.
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
- 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
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
- Help improve observability, operational visibility, and troubleshooting workflows
- Improve the customer experience through better processes and technical guidance
Benefits
- Discretionary bonus
- Meaningful equity component
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
