{"active":true,"apply_url":"https://jobs.ashbyhq.com/perplexity/60deb376-51b5-46c6-9e17-55377a5ef34e/application","company":{"claimed":false,"name":"Perplexity","slug":"perplexity","website":"https://www.perplexity.ai"},"countries":["GB"],"employment_type":"full-time","first_seen_at":"2026-10-15T12:23:43Z","getwork_url":"https://getwork.dev/jobs/ab-perplexity-60deb376-51b5-46c6-9e17-55377a5ef34e/","id":"ab-perplexity-60deb376-51b5-46c6-9e17-55377a5ef34e","injection_flag":false,"last_seen_at":"2026-10-28T19:30:10Z","location":"London; United Kingdom","published_at":"2026-04-13T19:40:57Z","regions":["europe"],"remote":"hybrid","role_family":"ml","salary":null,"seniority":"unspecified","stack":["python","c++","kubernetes","terraform","llm"],"stack_mentioned":["aws","pytorch"],"title":"Member of Technical Staff (AI Infrastructure Engineer)","untrusted_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.\n\nRESPONSIBILITIES\n\n- Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads\n\n- Manage and optimize Slurm-based HPC environments for distributed training of large language models\n\n- Develop robust APIs and orchestration systems for both training pipelines and inference services\n\n- Implement resource scheduling and job management systems across heterogeneous compute environments\n\n- Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure\n\n- Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm\n\n- Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services\n\n- Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands\n\nQUALIFICATIONS\n\n- Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management\n\n- Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization\n\n- Experience with deploying and managing distributed training systems at scale\n\n- Deep understanding of container orchestration and distributed systems architecture\n\n- High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)\n\n- Experience managing GPU clusters and optimizing compute resource utilization\n\nREQUIRED SKILLS\n\n- Expert-level Kubernetes administration and YAML configuration management\n\n- Proficiency with Slurm job scheduling, resource management, and cluster configuration\n\n- Python and C++ programming with focus on systems and infrastructure automation\n\n- Hands-on experience with ML frameworks such as PyTorch in distributed training contexts\n\n- Strong understanding of networking, storage, and compute resource management for ML workloads\n\n- Experience developing APIs and managing distributed systems for both batch and real-time workloads\n\n- Solid debugging and monitoring skills with expertise in observability tools for containerized environments\n\nPREFERRED SKILLS\n\n- Experience with Kubernetes operators and custom controllers for ML workloads\n\n- Advanced Slurm administration including multi-cluster federation and advanced scheduling policies\n\n- Familiarity with GPU cluster management and CUDA optimization\n\n- Experience with other ML frameworks like TensorFlow or distributed training libraries\n\n- Background in HPC environments, parallel computing, and high-performance networking\n\n- Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices\n\n- Experience with container registries, image optimization, and multi-stage builds for ML workloads\n\nREQUIRED EXPERIENCE\n\n- Demonstrated experience managing large-scale Kubernetes deployments in production environments\n\n- Proven track record with Slurm cluster administration and HPC workload management\n\n- Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure\n\n- Experience supporting both long-running training jobs and high-availability inference services\n\n- Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management","url":"https://jobs.ashbyhq.com/perplexity/60deb376-51b5-46c6-9e17-55377a5ef34e"}