AI Infrastructure Engineer
US$220,000 – US$405,000 / yr base salary
Posted 13 Apr 2026
Advertised as “Member of Technical Staff (AI Infrastructure Engineer)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3–5 yrs
- Management level
- Individual contributor
- Contract
- Full-time
| Base salary | US$220,000 – US$405,000 / yr |
|---|
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
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Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
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Manage and optimize Slurm-based HPC environments for distributed training of large language models
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Develop robust APIs and orchestration systems for both training pipelines and inference services
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Implement resource scheduling and job management systems across heterogeneous compute environments
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Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
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Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
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Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
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Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands
Qualifications
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Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
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Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
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Experience with deploying and managing distributed training systems at scale
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Deep understanding of container orchestration and distributed systems architecture
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High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
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Experience managing GPU clusters and optimizing compute resource utilization
Required Skills
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Expert-level Kubernetes administration and YAML configuration management
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Proficiency with Slurm job scheduling, resource management, and cluster configuration
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Python and C++ programming with focus on systems and infrastructure automation
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Hands-on experience with ML frameworks such as PyTorch in distributed training contexts