About the Role:

We are looking for a Member of Technical Staff 3 to join our Cloud Platform team and contribute to the development, operation, and reliability of our multi-tenant SaaS platform. You will work on backend systems and cloud infrastructure : building features, fixing real production problems, and growing into broader ownership over time.

This is a hands-on role with real ownership. You are expected to take a feature or problem end-to-end, write production-quality code, and work closely with more senior engineers on design and architecture.

What You Will Do:

Engineering & Development

- Build and maintain platform components across control plane and data plane

- Implement features for tenant provisioning, configuration management, and cluster operations

- Write clean, well-tested, production-grade code and participate actively in code reviews

- Debug and resolve issues in cloud-native, distributed production environments

Operations & Reliability

- Partner with SRE on observability, alerting, and incident response for services you own

- Improve reliability and operability of platform systems — reduce toil, improve monitoring, fix recurring issues

- Contribute to on-call and develop strong instincts for production system behavior

Collaboration & Growth

- Work closely with senior and principal engineers on design discussions and RFCs

- Collaborate with cross-functional teams across geographies

- Participate in hiring — conduct interviews and contribute feedback

What You Will Bring:

Cloud & Infrastructure

- 3-5 years of Solid hands-on experience with at least one of AWS, GCP, or Azure — compute, networking, IAM, and managed services

- Working knowledge of infrastructure as code — Terraform or equivalent — for provisioning and managing cloud resources

- Comfortable working with containerized workloads — Docker, Kubernetes basics (deployments, services, config maps, RBAC, namespaces)

Backend Engineering

- Strong programming skills in at least one backend language — Go, Java, Python, or equivalent

- Experience building and operating REST or gRPC APIs in production

- Good understanding of databases — relational and NoSQL — and how to use them reliably at scale

Distributed Systems

- Practical understanding of distributed systems — service dependencies, failure modes, retries, timeouts, and basic HA patterns

- Hands-on with observability — structured logging, metrics dashboards (Grafana, Datadog, or equivalent), and basic alerting; able to diagnose production issues using these tools

Security Fundamentals

- Awareness of cloud security basics — IAM least-privilege, secrets management, and network access controls

AI-Augmented Engineering

- Uses AI coding assistants — Claude, Cursor, Copilot — as a regular part of daily workflow for writing, debugging, and reviewing code

- Comfortable using AI tools to understand unfamiliar codebases, generate boilerplate, draft documentation, and speed up routine tasks

- Knows to review AI output carefully and apply judgment before committing or deploying

Good to Haves:

- Exposure to Kubernetes operators, controllers, or CRDs

- Familiarity with GitOps workflows — ArgoCD, Flux, or equivalent

- Basic understanding of multi-tenancy concepts — isolation, resource quotas, or tenant lifecycle

- Experience contributing to or building internal observability dashboards and alerting pipelines

What Success Looks Like:

In 3 months:

- Ramped up, productive, and shipping features with guidance

- Comfortable with the codebase, deployment process, and team workflow

- Engaging actively in code reviews and team discussions

In 6 months:

- Owning features end-to-end with increasing independence

- Reliable on-call contributor — able to diagnose and resolve common production issues

- Proactively improving the quality and observability of systems you work on

In 12 months:

- Trusted, high-output contributor on the team

- Beginning to mentor interns or junior engineers

- Taking on broader problems that span multiple components or systems