Platform Engineer
Pi Security · San Francisco ·
- Work mode
- Onsite
- Category
- Devops
Pi Security · San Francisco ·
Pi is building an agentic product security platform for teams that need to secure software at Pi is building an agentic product security platform for teams that need to secure software at the speed they build it.
Modern development is accelerating, but security knowledge is still scattered across code, tickets, documents, incidents, reviews, and the people who remember why decisions were made. Pi turns that context into institutional security memory, helping teams triage faster, remediate in context, prevent repeat vulnerability classes, and embed security guardrails where engineering work already happens.
We are building for a future where security is not a blocker at the end of the development process. It is part of how software gets designed, reviewed, shipped, and improved.
Read about Pi Security on Forbes!
We are looking for a Platform Engineer to help build and operate the infrastructure behind Pi’s agentic security platform.
You will work across cloud infrastructure, Kubernetes, infrastructure as code, CI/CD, observability, data infrastructure, and developer tooling to keep our product reliable, secure, and easy to ship. Your work will directly support the systems that ingest security context, analyze code and infrastructure, run agentic workflows, and deliver secure remediation into engineering workflows.
This role is hands-on. You will own production-grade infrastructure, improve deployment workflows, debug distributed systems, and help engineering teams move faster without weakening operational or security controls.
Build, maintain, and improve production cloud infrastructure using infrastructure as code.
Operate Kubernetes-based environments, including workloads, networking, ingress, autoscaling, and identity integration.
Improve CI/CD pipelines for application, worker, service, and infrastructure deployments.
Support core platform services across databases, caches, object storage, container registries, secrets, load balancing, and networking.
Help operate workflow orchestration, worker infrastructure, async processing systems, and agentic analysis pipelines.
Improve observability across logs, metrics, traces, alerts, dashboards, and production debugging workflows.
Harden infrastructure security across IAM, secrets handling, network boundaries, workload identity, and deployment permissions.
Partner with product and backend engineers working across frontend, backend, data, and AI-assisted security workflows.
Build and maintain internal platform tooling, runbooks, automation, and deployment workflows.
Debug production issues across infrastructure, application, worker, and database layers.
Help create the foundation that lets engineering teams ship quickly, safely, and confidently.
Strong experience operating cloud infrastructure in production.
Strong infrastructure-as-code experience, including modules, remote state, provider configuration, and safe change management.
Hands-on Kubernetes experience in production environments.
Familiarity with Helm, ingress controllers, load balancers, Kubernetes networking, service accounts, and workload identity patterns.
Experience operating PostgreSQL or similar relational databases, including availability, migrations, backups, and performance basics.
Comfortable debugging distributed systems using logs, metrics, traces, and cloud-native tooling.
Strong security instincts around IAM, secrets, network exposure, least privilege, and production access.
Ability to write useful automation in Python, TypeScript, Bash, or similar languages.
Good judgment around production changes, CI/CD safety, rollout strategy, incident response, and operational tradeoffs.
Ability to work in a fast-moving startup environment with ownership, autonomy, and good judgment.
AWS
Kubernetes
Terraform
Helm
GitHub Actions
Docker
Node.js
Bun
Python
TypeScript
PostgreSQL
Redis
Lexical, semantic, and hybrid search
Vector search
Graph-based data modeling
Workflow orchestration systems
Data ingestion and indexing pipelines
Experience with workflow orchestration systems.
Experience operating multi-environment SaaS infrastructure.
Experience with Kubernetes autoscaling, node groups, workload isolation, and production reliability patterns.
Experience with AI infrastructure, security products, developer tools, or code analysis systems.
Familiarity with TypeScript monorepos and Python service environments.
Experience supporting systems that process code, security findings, infrastructure metadata, or customer-specific context.
Deployments become safer, faster, and easier to reason about.
Infrastructure changes are well-tested, observable, and reversible.
Engineers have better tooling and fewer platform blockers.
Production systems are more reliable, secure, and understandable.
The platform helps Pi move quickly while preserving the trust, isolation, and control customers expect from a security product.