Staff Engineer, Platform (R6019)

San Diego · San Mateo · USD 160,000-290,000 / year · Onsite · Posted Sep 22, 2026

At a glance

Shield AI is looking for a Staff Platform Engineer to contribute to the Platform solutions and infrastructure that power Forge, the AI Factory.
This is a hands-on technical leadership role where you will define platform architecture, implement production software, and establish reusable operational patterns.
The Forge Platform Engineering team provides Kubernetes-native capabilities supporting autonomy development, simulation, testing, training, evaluation, deployment, and operational workflows.

Build Kubernetes-native platform services by developing and operating Kubernetes-based services, controllers, operators, deployment patterns, and runtime integrations that support distributed workloads across multiple environments.
Develop distributed orchestration capabilities by designing and building reusable primitives for authoring, scheduling, and scaling pipeline work.
Build reliable data-processing infrastructure by developing platform capabilities for data storage, ingestion, validation, transformation, and governance.
Develop highly extensible platform components that provide standardized tooling around authentication, authorization, observation, networking, routing, and secret management.
Create reference architectures to establish recommended deployment patterns, operating profiles, capacity guidance, benchmarks, reliability practices, and distribution approaches.
Advance observability and operability by establishing end-to-end metrics, logs, traces, structured events, dashboards, alerting, service-level objectives, operational diagnostics, and runbooks.
Partner with downstream teams across autonomy, ML Ops, simulation, test, infrastructure, product, and customer-facing domains to turn recurring distributed-systems problems into reusable platform capabilities.

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 tolerance.
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
Distributed execution and workflow technologies such as Ray, Temporal, Argo Workflows, Flyte, Dagster, Airflow, Prefect, Kubernetes Jobs, or comparable systems
Durable messaging and event-streaming technologies such as NATS JetStream, Kafka, Redpanda, Pulsar, RabbitMQ, SQS/SNS, or comparable systems
ETL/ELT, batch processing, event-driven data pipelines, CDC, schema evolution, data validation, artifact processing, or large-file transfer workflows
Service networking technologies and practices such as Envoy, service meshes, Kubernetes networking, and CNI plugins
Observability software such as OpenTelemetry, Prometheus, Grafana, Loki, Tempo, Jaeger, distributed tracing, structured logging, SLOs, service-level indicators, alerting, and incident-management practices
Terraform, Helm, ArgoCD, GitOps, Kubernetes package management, repeatable platform distribution, and Infrastructure as Code

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