Senior Software Engineer, Frontend Platform (R5790)

Shield AI•San Mateo, CA

10d

About The Position

Shield AI is hiring a full-stack engineer focused on customer-facing products, developer tools, and AI-assisted workflows. The role will help build Forge, Shield AI’s engineering platform for building, integrating, testing, and operating Hivemind capabilities. Forge connects autonomy developers with the applications, services, compute, data, and workflows required to progress from local development to deployed systems. Its primary design partners are Shield AI’s Hivemind developers, and it is also intended for adoption, extension, and integration by external engineering teams. The engineer will help evolve Forge into an integrated engineering environment with shared foundations for APIs, user interfaces, extensions, model integrations, and agent tools. The work spans frontend architecture, backend services, and model integrations, and will be validated through real engineering workflows. The platform is intended to help engineers understand complex systems, develop autonomy software, investigate failures, and deliver reliable capabilities faster. The successful candidate will work with simulation, autonomy, and machine-learning subject-matter experts, as well as infrastructure, security, product, and design teams. The position owns complete workflows across user experience, architecture, services, APIs, deployment, and operations, with work directly supporting development, validation, and delivery of autonomy software.

Requirements

Experience owning production full-stack applications from design and implementation through rollout and support.
Strong proficiency with TypeScript and React or an equivalent modern web stack.
Experience building production backend services in Go, Python, Rust, or a comparable language.
Experience designing typed, versioned APIs and contracts between independently deployed components.
Strong knowledge of web and distributed-system architecture, including state management, routing, asynchronous communication, browser security, authentication, authorization, and failure handling.
Experience building real-time and high-performance applications, including WebGL or WebGPU.
Sound judgment about when to extend an existing system, introduce an abstraction, or keep a solution intentionally small.
Clear communication skills and ability to work across frontend, backend, infrastructure, and product disciplines.

Nice To Haves

Experience with extensible application platforms, including plugin systems, micro-frontends, web sandboxing, Web Components, or VS Code extensions.
Experience with agent-tool integrations and protocols such as Model Context Protocol.
Experience with API gateways, reverse proxies, service catalogs, or policy-driven routing.
Experience with schema-driven API development using OpenAPI, JSON Schema, Protocol Buffers, gRPC, or generated clients.
Experience with Kubernetes and ingress or gateways.
Experience with composable user experiences such as dashboard builders, configurable layouts, data visualization, or collaborative application state.
Experience in robotics, autonomy, simulation, aerospace, or other domains where software interacts with real-world systems.
Experience shipping Electron applications.

Responsibilities

Build and operate a frontend-focused development platform embedded in workflows used to train, validate, and deploy the AI Pilot.
Design a composable UI architecture that allows applications to contribute apps, dashboards, commands, widgets, and shared resource context.
Define typed, versioned contracts between the platform and independently developed Forge applications.
Create workflows for teams to package, publish, configure, install, upgrade, and disable applications.
Enable Forge’s native AI experiences and external agent harnesses to understand active context, invoke authorized tools, navigate applications, and compose dashboards.
Deliver consistent application behavior across local-development and AI Factory environments.
Partner with simulation, autonomy, and ML subject-matter experts to build initial applications, validate the platform against real workflows, and incorporate lessons learned.
Work with infrastructure, security, product, and design teams to shape the platform’s architecture and engineering practices.
Own production full-stack applications from design and implementation through rollout and support.