Senior Software Engineer, Platform Engineering

The role

You will design, build, and operate microservices that automate fleet provisioning, lifecycle, and capacity management. You will work on GCP and managed Kubernetes to turn stakeholder requirements into platform capabilities.

What you will do

• Design, build, and operate microservices that automate fleet provisioning, lifecycle, and capacity management
• Extend existing platform services with new features that directly drive customer-facing delivery
• Turn ambiguous stakeholder requirements into clear, modular designs and drive them to production
• Build on GCP and managed Kubernetes, working closely with infrastructure and cross-functional teams
• Own the reliability and observability of the services you ship

Required qualifications

• 5+ years building and operating production backend or platform services
• Strong proficiency in at least one backend language such as Go, Python, Java, or C#
• Experience designing microservices and APIs (gRPC/REST) in distributed systems
• Hands-on experience with Postgres and event-driven systems such as Kafka
• A track record of leading projects end-to-end and raising the bar for engineers around you

Nice to have

• Experience building on GCP, AWS, or managed Kubernetes
• Capacity management or fleet/hardware lifecycle experience
• Neocloud or GPU cloud infrastructure experience
• Experience with Go
• Observability tooling (Prometheus, Grafana)

Benefits

• Industry competitive pay
• Restricted Stock Units
• Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents
• Employer contributions to HSA accounts
• Paid Parental Leave
• Paid life insurance, short-term and long-term disability
• Teladoc
• 401(k) with a 100% match up to 4% of salary
• Generous paid time off and holiday schedule
• Cell phone reimbursement
• Tuition reimbursement
• Subscription to the Calm app

About the company

Crusoe is a vertically integrated AI infrastructure company that owns and operates each layer of the stack to power AI workloads. They use an energy-first approach to solve the demand for AI compute.