Principal Platform Engineer

We’ve partnered with a recently funded AI-for-science startup building autonomous research agents designed to accelerate scientific discovery across domains like drug development, molecular design, and scientific analysis. Backed by significant early funding and operating at the cutting edge of AI infrastructure, the team is scaling quickly after major commercial traction and is now hiring a Principal Platform Engineer to own and scale the infrastructure powering their AI scientist platform.

This is a highly hands-on leadership role for someone who has deep Kubernetes expertise, enjoys operating with high ownership in startup environments, and wants to build infrastructure that directly enables breakthrough scientific research.

Join a rocket ship at the perfect inflection point. They just signed a major deal and are scaling rapidly, which means massive impact from day one.

This would be 4-5 days in-office in Dogpatch, San Francisco.

Key Responsibilities:

• Architect, build, and operate Kubernetes infrastructure supporting thousands of long-running, stateful AI agent workloads.
• Design and implement custom Kubernetes operators and CRDs using tools such as Kubebuilder, Operator SDK, and controller-runtime.
• Own the broader infrastructure platform across cloud infrastructure, observability, networking, storage, and scaling strategy.
• Lead and mentor a small platform engineering team while remaining heavily hands-on technically.
• Partner closely with backend, ML, and research teams to design reliable infrastructure patterns for AI and scientific computing workloads.
• Establish production-grade infrastructure practices around monitoring, alerting, observability, and incident response.
• Drive infrastructure reliability, scalability, and operational excellence across the platform.

Key Qualifications:

• 10+ years of experience in infrastructure or platform engineering with deep hands-on Kubernetes expertise.
• Proven experience architecting and operating Kubernetes platforms for long-running, stateful workloads rather than only stateless services or short-lived inference jobs.
• Strong experience building custom Kubernetes operators and CRDs using Kubebuilder, Operator SDK, controller-runtime, or similar tooling.
• Recent startup or small-team experience with end-to-end ownership of infrastructure platforms.
• Experience leading or mentoring small engineering teams.
• Strong cloud infrastructure experience across AWS, GCP, or Azure, including managed Kubernetes services (EKS/GKE/AKS).
• Familiarity with infrastructure-as-code tooling such as Terraform or Pulumi.
• Experience building observability and monitoring systems using tools like Prometheus, Grafana, Datadog, or similar.
• Background supporting ML infrastructure, scientific computing, or data-intensive platforms is highly valued.
• Strong communication skills and high EQ, with the ability to work cross-functionally with technical and non-technical stakeholders.