---
title: "Member of Technical Staff (AI Infrastructure Engineer)"
company: "Perplexity"
company_url: "https://www.remjobs.works/companies/perplexity"
url: "https://www.remjobs.works/job/perplexity-member-of-technical-staff-ai-infrastructure-engineer-5e1eb46e-a355-4271-85a1-eff0f24c0d7c"
apply_url: "https://jobs.ashbyhq.com/perplexity/598e1f7d-b802-4de2-99ac-90eb2bc33315"
workplace: onsite
location: "San Francisco"
employment_type: full-time
seniority: staff
role: ai-machine-learning
region: united-states
skills: ["aws", "cpp", "kubernetes", "llm", "python", "pytorch", "tensorflow", "terraform"]
date_posted: 2026-04-13T19:40:16.870Z
first_seen_by_remjobs: 2026-08-26T15:14:51.821Z
---

# Member of Technical Staff (AI Infrastructure Engineer)

**Perplexity** — San Francisco

Apply: https://jobs.ashbyhq.com/perplexity/598e1f7d-b802-4de2-99ac-90eb2bc33315

## About Perplexity

Perplexity LLC is an Executive Coaching firm for SME

## About the role

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters

#### Responsibilities

- Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads

- Manage and optimize Slurm-based HPC environments for distributed training of large language models

- Develop robust APIs and orchestration systems for both training pipelines and inference services

- Implement resource scheduling and job management systems across heterogeneous compute environments

- Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure

- Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm

- Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services

- Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

#### Qualifications

- Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management

- Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization

- Experience with deploying and managing distributed training systems at scale

- Deep understanding of container orchestration and distributed systems architecture

- High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)

- Experience managing GPU clusters and optimizing compute resource utilization

#### Required Skills

- Expert-level Kubernetes administration and YAML configuration management

- Proficiency with Slurm job scheduling, resource management, and cluster configuration

- Python and C++ programming with focus on systems and infrastructure automation

- Hands-on experience with ML frameworks such as PyTorch in distributed training contexts

- Strong understanding of networking, storage, and compute resource management for ML workloads

- Experience developing APIs and managing distributed systems for both batch and real-time workloads

- Solid debugging and monitoring skills with expertise in observability tools for containerized environments

#### Preferred Skills

- Experience with Kubernetes operators and custom controllers for ML workloads

- Advanced Slurm administration including multi-cluster federation and advanced scheduling policies

- Familiarity with GPU cluster management and CUDA optimization

- Experience with other ML frameworks like TensorFlow or distributed training libraries

- Background in HPC environments, parallel computing, and high-performance networking

- Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices

- Experience with container registries, image optimization, and multi-stage builds for ML workloads

#### Required Experience

- Demonstrated experience managing large-scale Kubernetes deployments in production environments

- Proven track record with Slurm cluster administration and HPC workload management

- Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure

- Experience supporting both long-running training jobs and high-availability inference services

- Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

---

Source: Perplexity's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/perplexity-member-of-technical-staff-ai-infrastructure-engineer-5e1eb46e-a355-4271-85a1-eff0f24c0d7c