Senior Staff Machine Learning Engineer

Job Description What you get to do in this role: • Lead the design and delivery of complex components and services within the cloud-native platform, owning them from design through production operation. • Act as the technical lead for major features and initiatives within the team, translating ambiguous problems into clear, scalable, and executable designs. • Partner with Principal and Distinguished Engineers to ensure alignment with broader platform architecture, engineering standards, and long-term technical strategy. • Identify technical risks early and drive them to resolution, with a strong focus on reliability, scalability, operability, and engineering excellence. • Maintain a strong hands-on presence across the development lifecycle, including designing, coding, and reviewing core systems such as Kubernetes operators, controllers, infrastructure automation, and platform services. • Mentor mid-level and junior engineers while raising the engineering bar through code reviews, design reviews, technical guidance, and collaborative problem-solving.

To be successful in this role you have: • Experience leveraging or critically thinking about how to integrate AI into engineering and platform work—AI-powered tooling, automated operational workflows, agentic systems for fleet visibility and operations, or reasoning about AI’s impact on how infrastructure is built and run. • 14+ years of experience designing, building, and operating large-scale distributed systems and cloud-native platforms in production environments. • Demonstrated experience building and operating Kubernetes platform infrastructure at scale, with ownership of platform capabilities through deployment, upgrades, and production operations. • Hands-on developing Kubernetes Operators, Admission Controllers, or other platform extensions using Kubebuilder, Operator Framework, or similar technologies. • Experience operating both managed Kubernetes services (EKS, AKS, GKE) and self-managed Kubernetes environments. • Experience supporting cloud-native database platforms, large-scale data infrastructure, or stateful services on Kubernetes. • Strong software engineering expertise in Go, with hands-on experience developing Kubernetes Operators, Controllers, Custom Resource Definitions (CRDs), and platform automation capabilities. • Deep understanding of Kubernetes architecture, including orchestration, scheduling, security, storage, networking, and workload management. • Proven experience operating stateful and distributed workloads on Kubernetes, including databases, data services, or other mission-critical platform components. • Experience with public cloud platforms (AWS, Azure, or GCP), including compute, networking, security, and identity management services. • Strong background designing and building microservices, event-driven systems, and distributed application architectures. • Experience owning complex technical initiatives end-to-end, from architecture and design through implementation, deployment, and operational support. • Hands-on experience with containers, CI/CD, GitOps practices, and Infrastructure-as-Code using tools such as Terraform, Crossplane, or equivalent technologies. • Experience implementing observability and reliability best practices, including metrics, logging, tracing, monitoring, alerting, and service-level objectives. • Proven ability to drive technical strategy through architectural design reviews, technical proposals, and cross-functional collaboration. • Good to have: • Experience with container networking (CNI) and/or service mesh. • Experience with mTLS, workload identity, and secrets management at fleet scale. • Experience with observability—metrics, tracing, and SLOs. • Experience with Terraform, Crossplane, or similar declarative infrastructure tooling. • Experience designing or tuning distributed data or compute workloads on Kubernetes. Distributed Systems Expertise • Experience with large‑scale deployments of distributed systems. •