AI Infra Engineer New York City; Palo Alto; San ...

Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world’s leading AI platforms. Perplexity has raised over $1B in venture investment from some of the world’s most visionary and successful leaders, including Elad Gil, Daniel Gross, Jeff Bezos, Accel, IVP, NEA, NVIDIA, Samsung, and many more. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Throughout human history, change and innovation have always been driven by curious people. Today, curious people use Perplexity to answer more than 780 million queries every month–a number that’s growing rapidly for one simple reason: everyone can be curious.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 work in a hybrid SRE/Dev Engineering capacity, partnering closely with our Infrastructure and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters.ResponsibilitiesDesign, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloadsManage and optimize Slurm-based HPC environments for distributed training of large language modelsDevelop robust APIs and orchestration systems for both training pipelines and inference servicesImplement resource scheduling and job management systems across heterogeneous compute environmentsBenchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructureBuild monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and SlurmRespond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference servicesOptimize cluster utilization and implement autoscaling strategies for dynamic workload demandsQualificationsStrong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster managementHands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimizationExperience with deploying and managing distributed training systems at scaleDeep understanding of container orchestration and distributed systems architectureHigh 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 utilizationRequired SkillsExpert-level Kubernetes administration and YAML configuration managementProficiency with Slurm job scheduling, resource management, and cluster configurationPython and C++ programming with focus on systems and infrastructure automationHands-on experience with ML frameworks such as PyTorch in distributed training contextsStrong understanding of networking, storage, and compute resource management for ML workloadsExperience developing APIs and managing distributed systems for both batch and real-time workloadsSolid debugging and monitoring skills with expertise in observability tools for containerized environmentsPreferred SkillsExperience with Kubernetes operators and custom controllers for ML workloadsAdvanced Slurm administration including multi-cluster federation and advanced scheduling policiesFamiliarity with GPU cluster management and CUDA optimizationExperience with other ML frameworks like TensorFlow or distributed training librariesBackground in HPC environments, parallel computing, and high-performance networkingKnowledge of infrastructure as code (Terraform, Ansible) and GitOps practicesExperience with container registries, image optimization, and multi-stage builds for ML workloadsRequired ExperienceDemonstrated experience managing large-scale Kubernetes deployments in production environmentsProven track record with Slurm cluster administration and HPC workload managementPrevious roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructureExperience supporting both long-running training jobs and high-availability inference servicesIdeally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource managementThe cash compensation range for this role is $190,000 - $250,000.Final offer amounts are determined by multiple factors, including, experience and expertise, and may vary from the amounts listed above.Equity: In addition to the base salary, equitymay be partof the total compensation package.Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan.Apply for this job*indicates a required fieldFirst Name *Last Name *Email *Phone *Resume/CV *Enter manuallyAccepted file types: pdf, doc, docx, txt, rtfEnter manuallyAccepted file types: pdf, doc, docx, txt, rtfWebsiteLinkedIn ProfileWill you now or in the future require visa sponsorship for employment? * Select...Perplexity has an office-centric work model with 4 days per week in the office from the San Francisco Bay Area or New York City. Are you willing to come in 4 days per week? * Select...If you are not based in any of these locations, are you open to relocation to San Francisco, Palo Alto, or New York City? * Select...What are you looking for in your next role? * #J-18808-Ljbffr

Created: 2025-09-17