Member of Technical Staff, Principal AI Infrastructure Engineer
Principal-level platform engineer who designs, scales, and operates the Kubernetes-based core infrastructure running thousands of persistent, stateful AI scientist agent workloads — building CRDs and operators, autoscaling, storage/networking, and observability. On-site in San Francisco with deep Kubernetes and cloud (EKS/GKE/AKS) expertise.
- Compensation
- $200K–$350K / yr
- Location
- San Francisco
- Work setup
- Onsite
- Seniority
- Principal
- Employment
- Full time
- Experience
- 10+ years
- Category
- Devops
- Posted
Skills
Must have
- Typically 10+ years of professional infrastructure or platform engineering experience
- Deep hands-on Kubernetes expertise in production environments
- Experience designing and implementing CRDs and Kubernetes operators (Kubebuilder, Operator SDK, or controller-runtime)
- Track record operating and scaling Kubernetes clusters supporting thousands of persistent or long-lived resources
- Deep understanding of Kubernetes internals (API server, etcd, scheduler, controller manager, kubelet) at scale
- Expertise with cloud infrastructure (AWS EKS, GCP GKE, or Azure AKS) and associated networking, storage, and IAM primitives
- Proficiency in at least one systems or backend language for operator development and infrastructure tooling
- Hands-on experience with infrastructure-as-code tools (Terraform, Pulumi, or Crossplane) and GitOps workflows
- Strong working knowledge of container networking (CNI plugins, service mesh, network policies), storage (CSI, persistent volumes, StatefulSets), and security (RBAC, Pod Security Standards, secrets man
- Ability to operate autonomously, make sound technical judgments, and drive projects from concept through production
Nice to have
- Experience with data-intensive platforms, scientific computing, or ML/AI infrastructure
- Prior experience in startups or small teams with significant architectural ownership and ambiguity
- Experience scaling systems, teams, or platforms through periods of rapid growth
Full description
About Us
Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI.
About the Role
As a Principal Member of Technical Staff you'll play a key role in designing, scaling, and operating the core platform infrastructure that powers autonomous scientific discovery. Your primary focus will be the orchestration for our agents at scale, building and managing clusters that orchestrate thousands of persistent, stateful workloads, developing custom resource definitions (CRDs) and operators, and ensuring the reliability and efficiency of our compute layer at scale.
Our mission is to build an AI scientist, and you'll own the infrastructure foundation it runs on. AI agents performing long-running scientific research demand resilient scheduling, lifecycle management, and resource orchestration far beyond typical cloud-native workloads. This role will influence platform architecture, establish infrastructure best practices, and partner closely with backend engineers, ML engineers, and researchers to deliver a production-grade environment that lets science move faster.
This position is part of the Platform Infrastructure team.
Key Responsibilities
- Architect, implement, and operate Kubernetes clusters that support thousands of concurrent, persistent resources (agents, jobs, services) with high availability and efficient resource utilization.
- Design and develop custom resource definitions (CRDs) and Kubernetes operators to model and manage domain-specific workloads such as AI agent lifecycles, research pipelines, and long-running compute tasks.
- Drive the strategy for cluster scaling, node pool management, autoscaling policies, and resource quota frameworks to handle rapid workload growth.
- Build and maintain infrastructure-as-code (Terraform, Pulumi, or similar) for reproducible, version-controlled environment management.
- Design and implement robust scheduling, placement, and affinity strategies to optimize cost, performance, and fault tolerance for heterogeneous workloads (CPU, GPU, memory-intensive).
- Establish and uphold best practices around observability, monitoring, alerting, and incident response for infrastructure systems (Prometheus, Grafana, Datadog, or similar).
- Own storage and networking strategy within Kubernetes — including persistent volume management, CSI drivers, service mesh, network policies, and ingress architecture.
- Troubleshoot complex, cross-system infrastructure issues and guide others through effective debugging and remediation in distributed environments.