AI Platform Support Engineer – APAC

🔥 2 hours ago

🌐 Philippines, Singapore – Remote

🇵🇭 Philippines – Remote 🇸🇬 Singapore – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Lightning AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS

Lightning AI is a platform designed to build machine learning models and publish Lightning Apps, which are templates that integrate various ML lifecycle tools. The company specializes in artificial intelligence, machine learning, infrastructure, deep learning, and data science, positioning itself as a tool to expedite the ML development process from months to days.

📋 Description

• 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

🎯 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

🏖️ Benefits

• 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

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