AI Platform Support Engineer – APAC

This is a fully remote position, open to applicants in Philippines, +1 more country.

📋 Description

• Collaborate closely with customer engineering teams managing training and inference workloads in a production environment.

• Assist clients in diagnosing and resolving intricate issues related to distributed systems and ML infrastructure.

• Serve as a technical advisor during critical incidents and platform degradation scenarios.

• Convert infrastructure-related challenges into actionable insights for ML engineers.

• Examine failures associated with distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems.

• Troubleshoot issues related to PyTorch, CUDA, NCCL, and inference serving.

• Analyze logs, metrics, traces, and system behavior to identify root causes.

• Debug containerized workloads across Kubernetes and bare-metal GPU environments.

• Assist customers in scaling workloads across multi-node GPU systems.

• Identify and diagnose performance bottlenecks in compute, memory, networking, and storage.

• Recognize recurring customer issue patterns and facilitate long-term improvements in reliability.

• Contribute to post-incident reviews and enhance operational practices.

• Develop internal tools, automation, documentation, and runbooks.

• Collaborate with infrastructure, networking, and platform engineering teams.

• Enhance observability, operational visibility, troubleshooting workflows, processes, and technical guidance.

⛳️ Requirements

• Strong background in software engineering and systems troubleshooting.

• Experience with Kubernetes and containerized environments.

• Proficient knowledge of Linux systems, including networking, storage, process management, and performance tuning.

• Familiarity with cloud infrastructure and distributed systems.

• Proficient with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry.

• Hands-on experience managing machine learning workloads in production or research settings.

• Experience with distributed ML systems and tools like PyTorch, CUDA, or NCCL.

• Understanding of GPU infrastructure and orchestration.

• Experience in troubleshooting performance, reliability, or scaling challenges in ML infrastructure.

• Insight into the operational challenges of running ML systems at scale.

• Excellent communication skills and the ability to engage directly with highly technical clients and engineering teams.

• Comfortable working in fast-paced, ambiguous environments.

• Passionate about collaboratively solving complex technical challenges.

🏝️ Benefits

• Medical, dental, and vision coverage for employees and their eligible dependents.

• RSUs that provide employees with 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 work schedules.

• Complimentary meals at office hubs.

• Benefits may vary by location, team, and role.