Senior Full-Stack Engineer, Forge Platform

About the role

Shield AI is a defense technology company building autonomy systems for air, maritime, space and other platforms. Forge is its engineering platform used to build, integrate, test and operate Hivemind capabilities, connecting developers with the applications, services, compute, data and workflows they need. This role is for a full-stack engineer who will build tools to develop, train and validate autonomous systems, working with Hivemind Pilot developers, Hivemind Solutions teams and domain experts.

Key responsibilities

• Build complete features across React interfaces, backend services, APIs and data storage.
• Translate complex technical workflows into intuitive applications with clear progress, results and diagnostic paths.
• Design and evolve APIs and data models that support applications, automation and integrations with other teams.
• Contribute to the platform architecture through requirements or direct contribution.
• Build software that runs locally, in CI/CD pipelines and in air-gapped Kubernetes clusters.
• Test and support what you build using logs, metrics, traces and user feedback.
• Work with users, product, design and engineering teams to define scope, make trade-offs and deliver iteratively.

Required profile

• Experience delivering and supporting production applications, with contributions to both frontend and backend.
• Ability to reason about service interactions, authentication, authorization, background jobs and failures across application boundaries.
• Familiarity with containers, CI/CD and deploying and debugging services in a shared environment.
• Clear communication, engineering judgment and the ability to take work from an ambiguous problem to a supported solution.

Required skills

• TypeScript and React, including component design, state management, asynchronous data handling and API integration.
• Backend services in Go, Python or a comparable language, including API design, validation and error handling.
• Databases and application data models, including querying, persistence and schema changes.

Preferred: hands-on Kubernetes experience (including Helm charts), developer tools for simulation, training or test and evaluation, workflow tools such as Ray, Argo Workflows or Dagster, OpenAPI, gRPC or WebSockets, and experience with cloud, on-premises, edge or air-gapped deployments.