Staff Engineer, Platform (R6019)

Job description

Job responsibilities

• Build Kubernetes-native platform services: Develop and operate Kubernetes-based services, controllers, operators, deployment patterns, and runtime integrations that support distributed workloads across multiple…
• Develop distributed orchestration capabilities: Design and build reusable primitives for authoring, scheduling, and scaling pipeline work.
• Build reliable data-processing infrastructure: Develop platform capabilities for data storage, ingestion, validation, transformation, and governance.
• Develop highly extensible platform components: The Forge Platform base provides standardized tooling around authentication, authorization, observation, networking, routing, secret management, and more to the services…
• Create reference architectures: Establish recommended deployment patterns, operating profiles, capacity guidance, benchmarks, reliability practices, and distribution approaches across cloud providers, on-prem, edge,…
• Advance observability and operability: Establish end-to-end metrics, logs, traces, structured events, dashboards, alerting, service-level objectives, operational diagnostics, and runbooks for workflows, pipelines,…
• Partner with downstream teams: Work directly with autonomy, ML Ops, simulation, test, infrastructure, product, and customer-facing teams to turn recurring distributed-systems problems into reusable platform capabilities.

Job requirements

• Significant experience designing and operating production distributed systems, cloud-native platforms, backend infrastructure, or data-intensive services.
• Strong software engineering skills and a record of delivering production systems in Go and Python.
• Deep understanding of distributed-systems fundamentals, including failure handling, idempotency, consistency tradeoffs, retries, ordering, delivery semantics, backpressure, partitioning, state management, and fault…
• Experience designing or operating workflow orchestration, distributed job execution, asynchronous processing, event-driven systems, or long-running service workflows.
• Ability to define architecture and technical standards while remaining hands-on in implementation, production troubleshooting, performance analysis, and reliability improvement.
• Experience working across multiple teams to turn recurring infrastructure needs into reusable, well-documented platform capabilities.
• Clear technical communication and the ability to make complex distributed-systems architecture understandable to both specialists and downstream users.
• Kubernetes controllers, operators, Custom Resource Definitions, admission control, scheduling extensions, KubeRay, or workload-management systems.