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Member of Technical Staff (AI Infrastructure Engineer)

Perplexity

Member of Technical Staff (AI Infrastructure Engineer) is a still-open role at Perplexity on Jobrow. Location: On-site · San Francisco · California · United States. Listed pay: $220k–$405k. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$220k–$405k
Workplace
On-site
Location
San Francisco · California · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this role

We are looking for an AI Infra engineer to join our growing team.

  • Function: Machine learning
  • Seniority: staff
  • python
  • aws
  • kubernetes
  • pytorch

Location & pay

On-site · San Francisco · California · United States Listed pay on Jobrow: $220k–$405k.

Source

Jobrow reads the public Ashby board for Perplexity. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

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 a

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