AI Platform Support Engineer – APAC

• 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
• 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 across Kubernetes and bare-metal GPU environments
• Support customers scaling workloads across multi-node GPU systems
• Diagnose compute, memory, networking, and storage performance bottlenecks
• Identify recurring customer-issue patterns 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, troubleshooting workflows, processes, and technical guidance

• 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

• Medical, dental, and vision coverage for employees and eligible dependents
• RSUs that give employees a stake in the company's long-term success
• 401(k) matching (U.S.) and pension contributions (U.K.)
• Unlimited PTO, company holidays, and floating holidays
• Two weeks of company closure each winter
• 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
• Complimentary meals at office hubs
• Benefits may vary by location, team, and role