Staff Engineer, Platform (R6019)

The role

Build and operate Kubernetes-native platform services and distributed orchestration capabilities for the Forge Platform, the AI Factory. Define platform architecture and reference patterns, and work with autonomy, ML, simulation, test and product teams to turn recurring distributed-systems problems into reusable platform capabilities.

What you will do

• Build and operate Kubernetes-based services, controllers, operators and runtime integrations for distributed workloads across environments.
• Design and build reusable primitives for authoring, scheduling and scaling pipeline work.
• Build platform capabilities for data storage, ingestion, validation, transformation and governance.
• Develop extensible platform components for authentication, authorization, observability, networking, routing and secret management.
• Create reference architectures, deployment patterns, capacity guidance, benchmarks and reliability practices across cloud, on-prem, edge and air-gapped environments.
• Establish metrics, logs, traces, dashboards, alerting, SLOs and runbooks for workflows, pipelines, event streams and platform services.
• Partner with autonomy, ML Ops, simulation, test, infrastructure, product and customer-facing teams.
• Define platform architecture and technical standards while staying hands-on in implementation and production troubleshooting.

Required qualifications

• 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: failure handling, idempotency, consistency tradeoffs, retries, ordering, delivery semantics, backpressure, partitioning, state management and fa
• 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, able to explain complex distributed-systems architecture to specialists and downstream users.

Nice to have

• 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 or Kubernetes Jobs.
• Durable messaging and event-streaming technologies such as NATS JetStream, Kafka, Redpanda, Pulsar, RabbitMQ or SQS/SNS.
• ETL/ELT, batch processing, event-driven data pipelines, CDC, schema evolution, data validation or large-file transfer workflows.
• Service networking technologies such as Envoy, service meshes, Kubernetes networking and CNI plugins.
• Observability tools such as OpenTelemetry, Prometheus, Grafana, Loki, Tempo, Jaeger, distributed tracing and SLO practices.
• Terraform, Helm, ArgoCD, GitOps, Kubernetes package management and Infrastructure as Code.

Benefits

• Bonus
• Benefits (full-time regular employees)
• Equity (full-time regular employees)
• Temporary benefits package, applicable after 60 days of employment (temporary employees)

Before you apply

• All offers are contingent on a cleared background check and possible reference check.
• Military fellows and part-time employees are not eligible for benefits.

About the company

Shield AI is a venture-backed defense-tech company that builds intelligent systems to protect service members and civilians. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies.