---
title: 'Member of Technical Staff (AI Infrastructure Engineer) at Perplexity'
canonical: 'https://feeny.ai/job/member-of-technical-staff-ai-infrastructure-engineer-perplexity-london-prt2jzs3xr02'
type: 'job'
last_seen: '2026-09-06'
---

# Member of Technical Staff (AI Infrastructure Engineer) at Perplexity

- **Company:** [Perplexity](https://feeny.ai/companies/perplexity)
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-04-13
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/Perplexity/60deb376-51b5-46c6-9e17-55377a5ef34e/application

**Skills:** Kubernetes, Slurm, Python, C++, PyTorch, AWS, YAML, Distributed Systems, API Development, Observability Tools, Container Orchestration, Kubernetes Operators, Multi-cluster Federation, GPU Cluster Management, CUDA Optimization, TensorFlow, HPC Environments, Parallel Computing, High-performance Networking, Terraform

> The AI Infrastructure Engineer designs, deploys, and optimizes large-scale AI training and inference clusters using Kubernetes, Slurm, and AWS. This role involves managing distributed training systems, implementing autoscaling strategies, and ensuring high availability for critical ML workloads.

## Job description

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

## About Perplexity

## Company Overview
- **One-liner**: Perplexity builds an AI-powered answer engine that delivers accurate, real-time answers to any question, serving curious minds and professionals who demand precision and speed.
- **Entity Type**: Private (total funding $1.7B across 16 rounds)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2022
- **Founders**: Aravind Srinivas, Denis Yarats, Johnny Ho, Andy Konwinski

## Core Business
- **Primary industry**: AI-powered search and knowledge retrieval, Software Development
- **Target customers**: B2C (general users), B2B (enterprise API customers), and power users seeking reliable information
- **Mission**: “AI for people who expect more – more accuracy, more agency, more signal, less noise.”

## Products & Services
- **Perplexity AI (Free Tier)**: Real-time conversational search engine that synthesizes web results with citations, accessible via web and mobile app. *Type*: SaaS
- **Perplexity Pro**: Subscription offering enhanced features such as advanced AI models, multimodal search, and increased usage limits. *Type*: SaaS (subscription)
- **Perplexity API Platform**: Enables developers to integrate Perplexity’s search and reasoning capabilities into their own applications. *Type*: API
- **Perplexity Browser Extension**: Brings AI-powered search directly into the browser for quick answers without leaving the page. *Type*: Browser extension

## Market Standing
- **Valuation/Market Cap**: Not publicly available in provided sources (prior reports indicate ~$9B valuation as of 2024, but not confirmed in this data set)
- **Key Metric**: Total funding $1.7B (16 rounds); annual revenue ~$50M (LinkedIn estimate)
- **Notable Investors/Partners**: Not explicitly listed in provided search results; widely known investors include IVP, NEA, Jeff Bezos, and others (not sourced here)
- **Growth Signals**: Headcount grew 11.3% YoY to 1,152 employees; operates in 54 countries; LinkedIn followers grew 131.8% yearly to >1.6M; active hiring across 48+ open roles including AI Research, Engineering, and Go-to-Market teams

## Competitive Advantages
- **Real-time, cited answers**: Unlike traditional chatbots, Perplexity grounds answers in live web results with transparent sources, building trust and accuracy.
- **Multimodal and agentic capabilities**: Continuously pushes frontier research (e.g., query-aware context compression, agents for knowledge work) that differentiates from generic LLM interfaces.
- **Vertical integration from research to product**: In-house AI research team tackles open problems unique to Perplexity’s scale, enabling fast iteration on novel methods.
- **Strong brand and user base**: Rapid consumer adoption with millions of monthly active users and high engagement.

## Strategic Focus
- **Pushing the frontier of search**: Evolving from retrieval services to code generation, agents, and full autonomy in knowledge work.
- **Expanding enterprise and API revenue**: Growing B2B footprint with API platform and enterprise solutions.
- **Global hiring and office expansion**: Offices in San Francisco, Palo Alto, New York, Austin, Washington D.C., London, Berlin, and Belgrade, with “4 days in office” hybrid model.
- **Investing in AI research**: Dedicated research division (Perplexity Research) exploring reasoning, agents, and systems to redefine how people navigate the internet.

## Why Work Here
- **Culture & Environment**: “Thoughtful support” culture with emphasis on craftsmanship, ownership, entrepreneurship, scholarship, and partnership. Hybrid schedule (4 days in-office) based on location.
- **Perks**: Daily catered meals (breakfast, lunch, dinner in office), office snacks, ride home program, commuter benefits, home office reimbursement, fitness classes, gym memberships.
- **Compensation & Benefits**: Competitive salary and equity packages; retirement plans with company match; flexible PTO; holiday shutdown between Christmas and New Year; 12 weeks paid bonding leave; comprehensive health, dental, and vision insurance.
- **Relocation & Visa Support**: Relocation assistance and visa/green card sponsorship for eligible roles. Partners with immigration firm to support the process.
- **Engineering Excellence**: “Technical assessment for relevant roles”; values hands-on contribution, broad skill sets, and merit-based evaluation. Frequent Tech Talks and exposure to greenfield AI research problems.

## Sources
1. [perplexity.ai/hub/careers](https://www.perplexity.ai/hub/careers)
2. [jobs.ashbyhq.com/perplexity](https://jobs.ashbyhq.com/perplexity)
3. [perplexity.ai](https://www.perplexity.ai/)
4. [linkedin.com/company/perplexity-ai](https://www.linkedin.com/company/perplexity-ai)
5. [research.perplexity.ai](https://research.perplexity.ai/)

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