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The pAGI Infra team builds and operates systems that enable large-scale model training and evaluation to be reliable, efficient, and easy to run. The infrastructure spans distributed training, inference and grading platforms, compute scheduling, and research tooling. The team partners closely with researchers and engineering teams to translate research needs into dependable infrastructure, improve GPU efficiency, and accelerate the path from experiment to validated model.
As an AI Systems Engineer, you will own projects end-to-end—from identifying bottlenecks and designing solutions through deployment and operation. The work combines distributed systems engineering, performance optimization, and close collaboration with research teams.
Key responsibilities:
- Build and operate infrastructure for large-scale training and evaluation, improving reliability, throughput, and resource efficiency
- Develop shared inference and grading platforms with automated capacity management, health monitoring, and performance visibility
- Improve compute scheduling and resource allocation to reduce idle GPU time and enable workloads to recover quickly from failures
- Diagnose bottlenecks across training, inference, and orchestration layers; work across teams to improve end-to-end performance
- Build self-service tools, automated validation, and observability that help researchers launch experiments, diagnose issues, and compare results with minimal manual intervention
Requirements:
- Strong software engineering fundamentals and experience building or operating large-scale distributed systems
- Experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling
- Highly self-motivated and comfortable taking ownership of open-ended problems
- Ability to debug across system boundaries and use measurements to guide improvements in performance and reliability
- Excitement about the potential of personal AGI and desire to build infrastructure that enables it
