About The Role

Join our team as a Staff Software Engineer, where you'll take on a leadership role in shaping technical direction and mentoring engineers. You'll work across control plane systems, influence cross-team roadmaps, and drive modernization efforts. This role includes participation in an on-call rotation and offers a range of benefits, including medical, dental, and vision insurance, flexible time off, and work from home opportunities.

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.
Deep expertise in Kubernetes internals and operations, including networking, scheduling, scaling, and controller patterns
Strong experience with Helm, container orchestration patterns, and CI/CD automation
Experience operating across multiple cloud providers (AWS, GCP, Azure) and/or hybrid environments
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
Comfortable working with IaC (Terraform, Pulumi) and GitOps workflows
Proven ability to design and build systems from scratch, making pragmatic tradeoffs along the way
Ability to influence without authority and align diverse stakeholders around technical decisions
A growth-oriented mindset—driven to teach, learn, and improve systems as well as people
7-10+ years of engineering experience, with at least 5+ in infrastructure, platform, or backend systems roles
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
Experience working with GPU infrastructure for training and inference
Past success driving infrastructure transformation or decomposing legacy systems

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