About the TeampAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research tooling. We partner closely with researchers and engineering teams to turn new research needs into dependable infrastructure, improve GPU efficiency, and shorten the path from an experiment to a validated model.
About the RoleWe're looking for an AI Systems Engineer to help scale the infrastructure behind our training and evaluation workflows. You'll own projects from identifying bottlenecks and designing solutions through deployment and operation. The work combines distributed systems engineering, performance optimization, and close collaboration with researchers. You might build a shared grading service, improve resource allocation across workloads, or bring a new training stack into production - directly improving how quickly and reliably research moves forward.
In this role, you will:- 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 visibility into performance.
- Improve compute scheduling and resource allocation to reduce idle GPU time and help workloads recover quickly from failures.
- Diagnose bottlenecks across training, inference, and orchestration, and 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 less manual intervention.
You might thrive in this role if you:- Are excited about the potential of personal AGI and want to build the infrastructure that enables it.
- Have strong software engineering fundamentals and experience building or operating large-scale distributed systems.
- Have experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling.
- Are highly self-motivated and comfortable taking ownership of open-ended problems.
- Enjoy debugging across system boundaries and using measurements to guide improvements in performance and reliability.
