Senior Software Platform Engineer, PsiQuantum

• The right person for this role is comfortable making judgment calls under uncertainty; they will have a lot of latitude, and accountability to match
• Experience: 5+ years in Platform Engineering, DevOps, or SRE roles
• Production AWS experience: Built and maintained systems on ECS/EKS, managed multi-account networking (VPCs, security groups), and dealt with real-world infrastructure complexity
• Infrastructure as Code: You've written and maintained Terraform (or Pulumi/CDK) in production, including applying ongoing changes as requirements evolved
• CI/CD: Improved build pipelines in production (reduced build times, increased reliability, made deployments easier to debug), including experience with GitLab CI, GitHub Actions, or equivalent
• GPU/HPC experience: Supported GPU workloads in production environments, including code optimization, CUDA debugging, and job scheduler setup

Desirable

• PsiQuantum's Applications Software Engineering Team builds tools for quantum algorithm developers: cloud development environments, circuit design tools, and resource estimation systems that help researchers write, simulate, and optimize quantum algorithms for the world's first utility-scale, fault-tolerant quantum computer
• We're hiring a platform engineer who bridges software infrastructure with GPU-accelerated computing, someone who can improve our AWS platform while helping researchers run quantum simulations efficiently on GPU clusters
• We're looking for someone to partner with our existing platform engineer, splitting responsibilities across a growing platform and making it easier for quantum researchers to do their best work
• That platform includes AWS infrastructure, Terraform configs, CI/CD workflows, and the GPU clusters our researchers depend on for large-scale, computationally intensive quantum simulations. There's a real opportunity to shape how all of it evolves
• Own our AWS infrastructure end-to-end and actively shape how it evolves; building, not just maintaining
• Reduce friction in the deployment pipeline so developers can ship without infrastructure blockers
• Harden systems with intention: lock down IAM roles, container images, and authentication flows in ways that reflect a clear understanding of where the real risks are
• Implement monitoring and alerting that catches production issues before users notice them
• Make deployments faster to roll out, easier to roll back, and less prone to failure
• Lead incident response and post-mortems when necessary
• Make GPU clusters and other infrastructure invisible to the researchers running it
• Own CUDA compatibility and driver versions across heterogeneous GPU clusters
• Build standardized SLURM job submission workflows that researchers can use without help
• Package and containerize Python simulation code for reproducible execution
• Monitor job health across utilization, cost, and runtime efficiency