Edison Scientific
Principal Platform Engineer
San Francisco, CA · Principal
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
$21,187 more — $221,187 — moves this role to Level IV and 4 lottery entries. That figure is inside the range the employer already advertised.
Based on the DOL prevailing wage for this occupation and worksite, a base salary of $221,187 would place this position at wage Level IV. That figure is within the posted range, and I'd like to target it. This role classifies under "Software Developers" for prevailing-wage purposes.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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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.
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).
- 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.
Requirements
- 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).
- 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.
- 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
Compensation
- $200,000 - $350,000 • Offers equity
- Competitive salary and equity
- Full healthcare coverage - we pay 100% of premiums for you and your dependents
Benefits
- Full healthcare coverage - we pay 100% of premiums for you and your dependents
- Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
- $300 health and wellness benefit
Company info
- This position is part of the Platform team.
This listing is sourced directly from Edison Scientific's careers page and normalized into a canonical job model.