Perplexity

Perplexity

Member of Technical Staff (Software Engineer, Inference & Training Platform)

San Francisco · Staff+

Sponsorship not specifiedDetected 5 days ago
GoRustNode.jsDistributed SystemsAWSGCPCloud PlatformsKubernetesPrometheusGrafanaMachine Learning

About the role

  • Perplexity serves hundreds of millions of queries a month, and every one of them fans out into multiple AI inference requests running in real time.
  • If you're excited about this role, we encourage you to apply even if your experience doesn't match every qualification listed above.

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.
  • 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.

Requirements

  • You've supported both long-running training jobs and high-availability inference services, and you know why they pull infrastructure in opposite directions.
  • GPU kernel work in CUDA or Triton - not required, but notable.
  • We expect you to have real depth in most of these:
  • Deep Kubernetes experience - custom operators, CRDs, and multi-cluster federation, not just running kubectl apply.
  • You've managed GPU clusters at scale: NVIDIA hardware, CUDA, and the networking that makes them fast (InfiniBand or RoCE).
  • You've orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar) and understand how different each one really is.
  • Strong distributed systems fundamentals: scheduling, resource allocation, and fault tolerance under load.
  • You write infrastructure and systems-level code in Go, Rust or C++.
  • You own problems end-to-end and do well when the path forward isn't laid out for you.
  • Inference serving stacks: vLLM, SGLang, or TensorRT-LLM.
  • Slurm or other HPC schedulers.
  • High-speed interconnects: InfiniBand, RoCE, or RDMA in production.

Skills

  • Behind that sits a large GPU fleet spread across several cloud providers.
Apply directly at Perplexity →Create a free account for alerts like thisView Perplexity immigration profile

This listing is sourced directly from Perplexity's careers page and normalized into a canonical job model.