Senior DevOps / Infrastructure Engineer | AI & Scientific Computing Location: Boston / Cambridge, MA (Hybrid, 3 days onsite) Employment: Full-time Experience: 5+ years preferred About the Opportunity We're partnering with a well-funded AI research startup building the infrastructure behind a new generation of scientific discovery. The company is developing an AI-native physics laboratory, combining AI agents, simulation and scientific verification. Its initial focus is addressing power and cooling constraints in AI data centers. Having raised $58M in seed funding, the company has assembled a team of approximately 27 people, with backgrounds spanning NVIDIA, Google, MIT, Harvard and Carnegie Mellon. This is an opportunity to join early, take significant ownership and shape the infrastructure powering advanced AI research. The Role We're looking for a Senior DevOps / Infrastructure Engineer to build and scale cloud, GPU and deployment infrastructure. You'll own the journey from code commit to reliable production deployment, developing the automation and tooling that enables researchers and engineers to move quickly. What You'll Be Doing • Build CI/CD pipelines supporting automated testing and reliable releases. • Manage cloud and GPU infrastructure using Infrastructure as Code. • Deploy and operate Kubernetes across cloud providers. • Own networking, DNS, recovery and multi-account infrastructure. • Build self-service tooling and automate operational workflows. • Lead incident investigation and improve reliability, observability and cloud costs. What We're Looking For • 5+ years of experience building and operating production infrastructure. • Strong Kubernetes experience and expertise in AWS, GCP or Azure. • Proven experience building CI/CD pipelines and Infrastructure as Code from scratch. • Experience with multi-account cloud environments and networking. • Strong incident management, troubleshooting and automation skills. • Ability to take independent ownership in a fast-moving environment. Nice to Have • GPU infrastructure or AI/ML compute experience. • Terraform, Pulumi or similar tooling. • FinOps, observability and cloud cost optimization. • Experience building internal developer platforms or self-service infrastructure.