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 mana
Member of Technical Staff (AI Inference Engineer)
Market pay estimate
£392,500–£392,500 / year for comparable Member of Technical Staff roles in United Kingdom. Not employer-provided.
Role overview
Job description
We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL. Responsibilities: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through
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What they are looking for
Skills & requirements
Qualification
3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation). Final offer amounts are determined by multiple factors including experience and expertise. Equity: In addition to the base salary, equity may be part of the total compensation package.
Department · AI Research & Systems
Hiring company
Perplexity
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