Staff Software Engineer

What You’ll Do

• Architect and implement scalable, secure Kubernetes-based infrastructure for multi-cloud and hybrid environments.
• Lead technical direction for core Fleet initiatives—control plane services, tenancy models, deployment pipelines, observability layers, and more.
• Mentor engineers across the team, fostering a strong engineering culture of ownership, curiosity, and excellence.
• Drive modernization efforts—introducing patterns like GitOps, Policy-as-Code (Kyverno), Cilium networking, autoscaling, and better resource efficiency.
• Collaborate deeply with SRE, Platform, and Application teams to align infrastructure capabilities with real-world product demands.
• Champion best practices in CI/CD, reliability, container lifecycle management, and dev experience.
• Be a thought partner to engineering leadership and help shape how Fleet scales its impact across the company.

What We’re Looking For

• 7-10+ years of engineering experience, with at least 5+ in infrastructure, platform, or backend systems roles.
• Deep expertise in Kubernetes internals and operations, including networking, scheduling, scaling, and controller patterns.
• Proven ability to design and build systems from scratch, making pragmatic tradeoffs along the way.
• Strong proficiency in modern programming languages such as Python or Go.
• Experience building production-quality, reliable, and observable systems that are used across engineering organizations.
• A growth-oriented mindset—driven to teach, learn, and improve systems as well as people.
• Experience operating across multiple cloud providers (AWS, GCP, Azure) and/or hybrid environments.
• Strong experience with Helm, container orchestration patterns, and CI/CD automation.
• Comfortable working with IaC (Terraform, Pulumi) and GitOps workflows.
• Ability to influence without authority and align diverse stakeholders around technical decisions.

Nice to Have

• Familiarity with Cilium, Kyverno, KEDA, Gateway API, OPA, or similar technologies.
• Experience building and running multi-tenant SaaS platforms.
• Exposure to on-prem delivery models or regulated environments.
• Experience with performance tuning for large-scale data or compute workloads.
• Past success driving infrastructure transformation or decomposing legacy systems.
• Experience working with GPU infrastructure for training and inference.