Platform Lead, Multi-Cloud GPU Inference & Training

Perplexity; New York (NY); On-site; USD 250,000 - 485,000; Full time

Job summary

Perplexity is building a unified platform for running training and inference workloads on a diverse GPU fleet spanning multiple cloud providers. You will own the platform, hide its complexity, and enable inference engineers and researchers to launch jobs without managing GPU provisioning or provider-specific infrastructure.

Responsibilities include building the self-serve compute platform, operating the GPU fleet across providers, solving GPU scarcity with smart scheduling, and ensuring

Qualifications

• Deep Kubernetes experience with custom operators, CRDs, and multi-cluster federation.
• Experience managing GPU clusters at scale with NVIDIA CUDA and networking.
• Orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar).
• Strong distributed-systems fundamentals: scheduling, resource allocation, fault tolerance.
• Go, Rust or C++ for infra code.
• Experience supporting long-running training and high-availability inference.

Responsibilities

• Build a self-serve compute platform. Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure.
• Operate the GPU fleet. Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs.
• Solve for GPU scarcity. Build the scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right workload onto the right hardware under real constraints.
• Support two very different workloads. Keep long-running distributed training jobs healthy while simultaneously guaranteeing the availability and latency of production inference services on the same fleet.
• Own the Kubernetes for GPU orchestration. Write the operators and CRDs, and manage many clusters across providers so the platform behaves the same everywhere we run.
• Make failure boring. Build the fault tolerance, autoscaling, and observability that keep the fleet utilized and let workloads survive node loss, provider hiccups, and capacity shifts without human intervention.
• Set technical direction across teams. Partner with inference and cloud infrastructure engineers to turn operational constraints into a coherent platform architecture and roadmap.

Skills

Kubernetes

Go

Rust

C++

Distributed systems

Tools

CUDA

InfiniBand

Multi-cloud

Job description

Perplexity is building a unified platform for running training and inference workloads on a diverse GPU fleet spanning multiple cloud providers. You will own the platform, hide its complexity, and enable inference engineers and researchers to launch jobs without managing GPU provisioning or provider-specific infrastructure.

Responsibilities include building the self-serve compute platform, operating the GPU fleet across providers, solving GPU scarcity with smart scheduling, and ensuring