Principal Member of Technical Staff, Core Infrastructure

Edison Scientific, Inc.; San Francisco (CA); On-site; USD 200,000 - 350,000; Full time

Benefits offered by this job

Full healthcare coverage

Parental leave (12 weeks)

Mental health support

401(k) matching

Wellness benefit

Lunch provided in office

Team off-sites

Job summary

Edison Scientific, Inc. is building an AI-driven platform and seeks a Principal Member of Technical Staff to own the core infrastructure that runs autonomous scientific workloads.

You will architect and operate Kubernetes clusters, design CRDs and operators, and ensure resilient, scalable compute for thousands of long‑running tasks. You’ll collaborate with backend, ML, and research teams, implement infrastructure-as-code, and drive best practices for observability, security, and cost efficiency.

Qualifications

• Over 10 years of infra or platform engineering in production.
• Deep Kubernetes internals knowledge (API server, etcd, scheduler).
• Experience with CRDs and Kubernetes operators.
• Hands-on with IaC tools (Terraform, Pulumi, Crossplane).
• Familiar with cloud providers AWS/GCP/Azure networking, storage, IAM.

Responsibilities

• Architect, implement, and operate Kubernetes clusters at scale.
• Design CRDs and operators for domain workloads.
• Drive cluster scaling, autoscaling, and resource quotas.
• Manage IaC for reproducible environments.
• Design scheduling and affinity for heterogeneous workloads.
• Establish observability, monitoring, and incident response.

Skills

Kubernetes expertise

Cloud infra design

CRDs & Operators

Terraform/Pulumi

GitOps

Tools

Kubebuilder

Operator SDK

controller-runtime

Terraform

Pulumi

Crossplane

Job 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.

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.

At Edison Scientific, engineering at the senior level is about technical ownership and leverage- understanding how complex systems interact, making sound architectural tradeoffs, and building foundations that allow teams and science to move faster.

This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team that genuinely believes in what they’re building.

This position is part of the Platform Infrastructure team.

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.

• Collaborate closely with backend, ML, and research teams to understand workload requirements and translate them into reliable infrastructure patterns.

Qualifications

• Typically, 10+ 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).

• Deep understanding of Kubernetes internals — API server, etcd, scheduler, controller manager, kubelet — and how they behave 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 management).

• Ability to operate autonomously, make sound technical judgments, and drive projects from concept through production.

Bonus points for

• 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.

Salary

$200,000 - $350,000 Offers equity

Why join us?

We’re a fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it. We also offer full-time employees the following:

• Competitive salary and equity

• 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

• Mental health support through Rula, our in-network therapist and psychiatrist network with fast availability

• 12 weeks of paid parental leave for maternity, paternity, and adoption

• Pet care support with a yearly employer-funded stipend for your animal companions

• Commuter benefits so you can pay for transit and parking with pre-tax dollars

• 401(k) company matching

• $300 health and wellness benefit quarterly

• Lunch is on us every day you’re in the office, and dinner is on us when you’re working late

• Regular team off-sites and company events

Edison Scientific is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.