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# Infrastructure Engineer

- **Company:** The Crosby Company
- **Location:** New York, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Batch Processing, Cloud Computing, Continuous Integration, Python (Programming Language), Software Requirements Analysis, Delivery Pipeline, Backend, Containerization, Kubernetes, Data Pipelines, Docker
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/member-of-technical-staff-infrastructure-crosby-7917190

## About the Role

* 4+ years of experience in infrastructure engineering, SRE, or backend systems
 * Strong experience with cloud platforms (e.g., AWS, GCP) and modern infrastructure tooling
 * Proficient in at least one programming language (e.g., Python, Go) and comfortable building internal tools
 * Experience with containerization and orchestration (e.g., Docker, Kubernetes)
 * Strong understanding of CI/CD systems, monitoring, and observability
 * Highly pragmatic with strong judgment around reliability vs speed trade-offs
 * Ownership mindset - you take responsibility for systems end-to-end
 * Comfortable operating in fast-paced, ambiguous environments

## Description

As an Infrastructure Engineer at Crosby, you'll play a critical role in building and scaling the systems that power our platform. You'll be responsible for designing and maintaining reliable, scalable infrastructure across compute, storage, and deployment systems.

You'll work closely with backend, AI, and product engineers to ensure our systems are performant, observable, and easy to operate. This role is for someone who cares deeply about reliability and developer velocity - building infrastructure that enables teams to ship quickly and confidently.

What you'll do

 * Build and scale infrastructure: Design and maintain cloud infrastructure across compute, storage, and networking.
 * Own deployment systems: Develop and improve CI/CD pipelines and deployment workflows to enable fast, reliable releases.
 * Ensure reliability and performance: Implement monitoring, alerting, and observability systems to maintain high uptime and performance.
 * Support AI and data workloads: Optimize infrastructure for ML/AI systems, including batch processing, inference workloads, and data pipelines.
 * Improve developer velocity: Build tooling and systems that make it easier for engineers to build, test, and deploy software.
 * Collaborate across teams: Partner with engineering teams to ensure infrastructure supports product and system requirements.


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