Cloud Engineer

Princeton University•Princeton, NJ

9d•$130,000 - $140,000•Onsite

About The Position

The Accelerator seeks a Cloud Engineer to design, build, and operate the secure cloud infrastructure that powers large-scale academic research on the information environment. Working as part of a small, high-trust cross-functional team, this individual will contribute across the full stack — from infrastructure and DevOps to backend services and data pipelines — and will have meaningful ownership over the technical systems that enable researchers at Princeton and across a global consortium to do their work. This is a role for a senior, self-directing engineer who is equally comfortable designing architecture and writing code, and who takes satisfaction in building systems that are reliable, secure, and well-understood by the people who depend on them. The right candidate brings deep cloud expertise alongside strong software engineering fundamentals — someone who can own infrastructure end to end and contribute meaningfully to application development.

Requirements

5–8 years of experience in cloud engineering, DevOps, or a software engineering role with significant infrastructure ownership.
Strong proficiency in Python; experience with at least one additional language (TypeScript/Node.js, Go, or equivalent).
Deep hands-on experience with Azure cloud services, including compute, networking, storage, identity, and managed services; familiarity with Azure CAF landing zones, subscription governance, and resource management at scale.
Proficiency with Terraform, including module development, remote state management, and PR-based workflow; Bicep familiarity a plus.
Experience designing and implementing CI/CD pipelines, preferably using GitHub Actions.
Production experience with Kubernetes / AKS — deploys, scaling, health management, upgrades, and cluster operations.
Solid Docker and container image management skills; experience building and maintaining containerized services in production.
Azure networking and security fundamentals, including Private Link, network rules, NSGs, and RBAC; comfort managing secrets hygiene across environments.
Azure cost management and FinOps awareness: budget alerts, cost/cluster policies, anomaly detection and response.
Comfort operating in and improving existing codebases with limited live handoff — able to orient independently, read unfamiliar infrastructure, and contribute quickly without extensive documentation.
Experience with Databricks or equivalent large-scale data processing platforms.
Solid understanding of data security principles, IAM patterns, and compliance frameworks (SOC 2, HIPAA, ISO 27001, or equivalent).
Experience operating a self-hosted observability stack — specifically Grafana, Loki, and Prometheus — including patching, upgrades, and dashboard maintenance; equivalent stack experience considered.
Ability to work independently on complex, ambiguous problems and communicate technical decisions clearly to non-technical stakeholders.
Strong written communication skills; comfortable producing architecture documentation, runbooks, and technical specifications.
A combination of relevant work experience and education equivalent to 5–8 years of hands-on cloud engineering or software engineering experience, with a demonstrable record of owning and delivering complex infrastructure and software projects. A bachelor's degree in Computer Science, Engineering, or a related field is preferred but not required — equivalent professional experience will be considered.

Nice To Haves

Experience supporting research computing or academic data infrastructure environments.
Familiarity with ML infrastructure tooling (MLflow, Hugging Face Hub, model serving frameworks).
Experience with IRB-compliant research data environments or sensitive data handling at scale.
Frontend development experience (React or equivalent) — useful on a small cross-functional team.
Relevant certifications: Azure Administrator (AZ-104), Azure Solutions Architect (AZ-305), Azure DevOps Engineer (AZ-400), or equivalent.

Responsibilities

Design, deploy, and maintain cloud infrastructure on Azure, with responsibility for performance, cost-effectiveness, and reliability across research and production environments.
Architect and manage Databricks workspaces, including compute cluster configuration, access controls, and cost optimization for large-scale data processing workflows.
Manage Azure networking, storage, identity (Azure AD / Entra ID), and resource governance across multiple environments.
Implement infrastructure-as-code using Terraform and/or Bicep; maintain version-controlled, reproducible infrastructure definitions including modules, remote state management, and PR-based workflow.
Deploy, operate, and maintain AKS clusters running containerized workloads — including containerized data crawlers — managing deploys, scaling, health monitoring, patching, and upgrades.
Administer Azure Blob Storage, including lifecycle policies, redundancy configuration, and access tier management.
Manage Azure networking and security, including Private Link, network rules, RBAC, and secrets hygiene across environments.
Own Azure cost management: budget alerts, cost/cluster policies, anomaly detection and response, and FinOps practices to keep infrastructure spend predictable and efficient.
Design, build, and maintain backend services, APIs, and data pipelines using Python and/or TypeScript/Node.js.
Develop and maintain CI/CD pipelines using GitHub Actions, ensuring reliable and automated delivery of infrastructure and application changes.
Build and maintain internal tooling that improves the experience and efficiency of the research and operations teams.
Contribute to frontend integrations where needed; comfortable working across the stack on a small team.
Develop and support data pipelines for ingesting, transforming, and serving large-scale behavioral and social media datasets to researchers.
Implement and maintain infrastructure for machine learning workflows, including model serving, experiment tracking, and compute resource management.
Support integration with ML frameworks and tools (e.g., MLflow, Hugging Face, or equivalent) within the managed environment.
Implement and maintain security controls across all systems, including encryption at rest and in transit, identity and access management, network segmentation, and secrets management.
Design and operate environments meeting IRB, data governance, and institutional compliance requirements; ensure adherence to standards equivalent to SOC 2, HIPAA, or ISO 27001 as applicable.
Conduct regular security reviews, vulnerability assessments, and penetration test coordination; manage remediation tracking.
Implement audit logging, access controls, and data handling procedures for sensitive research data in compliance with IRB protocols and data use agreements.
Operate, patch, and upgrade the self-hosted observability stack — Grafana (dashboards), Loki (log aggregation), and Prometheus (metrics) — including security patching and version upgrades; implement and maintain alerting, distributed tracing, and platform-wide monitoring.
Own incident response, root cause analysis, and operational reliability for production systems.
Develop and maintain runbooks, architecture documentation, and operational procedures.