Edison Scientific

Principal Platform Engineer

San Francisco, CA · Principal

Sponsorship not specified$200k-$350kDetected 127 days ago

Node.jsAWSGCPAzureCloud PlatformsKubernetesTerraformPrometheusGrafanaDatadogPlatform EngineeringMachine LearningAgentic AIIncident ResponseResearch

About the role

Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that.
AI agents performing long-running scientific research demand resilient scheduling, lifecycle management, and resource orchestration far beyond typical cloud-native workloads.
This role is on-site at our San Francisco office in the Dogpatch neighborhood.

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).
Own storage and networking strategy within Kubernetes - including persistent volume management, CSI drivers, service mesh, network policies, and ingress architecture.
Collaborate closely with backend, ML, and research teams to understand workload requirements and translate them into reliable infrastructure patterns.

5+ years of professional infrastructure or platform engineering experience, with deep hands-on Kubernetes expertise in production environments.
Experience designing and implementing custom resource definitions (CRDs) and Kubernetes operators (using frameworks such as Kubebuilder, Operator SDK, or controller-runtime).
Track record of operating and scaling Kubernetes clusters supporting thousands of persistent or long-lived resources (stateful workloads, persistent pods, long-running jobs).
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 management).
Deep understanding of Kubernetes internals - API server, etcd, scheduler, controller manager, kubelet - and how they behave at scale.
Ability to operate autonomously, make sound technical judgments, and drive projects from concept through production.
Bonus points for:
$200,000 - $350,000

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.
401(k) company matching
Lunch is on us every day you're in the office, and dinner is on us when you're working late
Regular team offsites and company events
A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it

Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
$300 health and wellness benefit

This listing is sourced directly from Edison Scientific's careers page and normalized into a canonical job model.