AI Platform Support Engineer

Responsibilities

Customer and Incident Support

• Partner directly with customer engineering teams running training and inference workloads in production.
• 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.

ML Infrastructure and Distributed Workloads

• Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems.
• Troubleshoot PyTorch, CUDA, NCCL, and inference-serving 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.

Reliability and 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 with infrastructure, networking, and platform engineering teams.
• 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 and ability to work directly with highly technical customers and engineering teams.
• Comfort operating in fast-moving, highly ambiguous environments.
• Enjoyment of 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 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 and Benefits

• Anticipated annual base salary: £75,000–£95,000 GBP.
• Discretionary bonus and meaningful equity component.
• Comprehensive health coverage, including medical, dental, and vision coverage for employees and eligible dependents.
• Retirement savings and pension contributions in the U.K.
• Unlimited paid time off, company holidays, and floating holidays.
• Two-week company-wide winter break.
• Paid parental and family leave.
• Annual learning and development allowance.
• Wellness and work-from-home stipends.
• Four weeks of paid sabbatical leave after four years of service.
• Flexible schedules and a hybrid work model for office-based teams.
• Complimentary meals at office hubs.
• Benefits may vary by location, team, and role.