Software Engineer, Compute Infrastructure
Builds and optimizes large-scale compute infrastructure for AI workloads, spanning hardware automation, distributed systems, Kubernetes orchestration, networking, storage, and developer tools. Requires strong systems engineering experience in performance, reliability, and production infrastructure.
About the job
Responsibilities
- Build and deeply optimize reliable system software for large-scale compute systems that run some of the world's most demanding AI workloads
- Design and operate infrastructure across accelerators, CPUs, NICs, switches, networking protocols, storage, data centers, cluster orchestration, scheduling, and fleet health
- Profile, benchmark, and optimize training workloads across compute, memory, storage, networking, NCCL and collective communication, and cluster scheduling bottlenecks
- Create hardware-aware automation that makes provisioning, firmware and driver upgrades, incident response, and day-to-day operations faster and less error-prone
- Build CaaS, agent infrastructure, profiling, observability, benchmarking, and platform tools that help researchers, product engineers, and operators launch, debug, and optimize workloads with less friction
- Turn operational lessons into better systems, stronger abstractions, and clearer ownership boundaries across teams
- Collaborate across research, engineering, security, networking, hardware, and data center teams to make compute capacity more capable and easier to use
Qualifications
- Strong software engineering skills and experience building, operating, or improving production infrastructure systems
- Experience in one or more relevant areas such as distributed systems, operating systems, networking protocols, RDMA, NCCL or collective communication, storage, Kubernetes, scheduling, observability, reliability engineering, high-performance computing, GPU infrastructure, CaaS, agent infrastructure, hardware-aware performance optimization, benchmarking, developer experience, or infrastructure tooling
- Ability to debug complex system behavior across software, hardware, networking, and workload layers, then turn findings into robust improvements
- Comfort with ambiguity, strong ownership, and a bias toward practical, durable solutions
Skills
Kubernetes, Nccl, Rdma, Distributed Systems, High-Performance Computing, Gpu Infrastructure, Observability, Reliability Engineering, Storage Systems, Networking Protocols
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