Senior Engineer, Full-Stack - Forge (R6244)

Shield AI•San Mateo, CA

1d•Onsite

About The Position

Shield AI is seeking a full-stack engineer to contribute to the development of physical AI by building tools for the development, training, and validation of autonomous systems. The role involves working with TypeScript and React applications, backend services, APIs, and Python data workflows, focusing on extensibility and reuse. The engineer will integrate internal platforms and open-source software within local and Kubernetes environments. Key responsibilities include building capabilities for simulation, synthetic data generation, training, and test and evaluation, enabling engineers to transform ideas into proven autonomy capabilities. This position requires close collaboration with Hivemind Pilot developers, Hivemind Solutions teams, and domain experts to translate complex workflows into reliable applications and reusable services. The role offers ownership of features from conception through implementation, deployment, and support, appealing to a generalist who enjoys varied frontend and backend work and is interested in the systems connecting them. The engineer will also influence technical decisions and assist in deploying and troubleshooting containerized services with platform engineers. Practical Kubernetes experience is considered a plus.

Requirements

Experience delivering and supporting production applications, with meaningful contributions to both frontend and backend development.
Proficiency with TypeScript and React, including component design, state management, asynchronous data handling, and API integration.
Experience building backend services in Go, Python, or a comparable language, including API design, validation, and error handling.
Experience working with databases and application data models, including querying, persistence, and schema changes.
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, sound engineering judgment, and the ability to work directly with users and carry work from an ambiguous problem to a supported solution.

Nice To Haves

Hands-on Kubernetes experience deploying or troubleshooting applications, including Helm charts, service configuration, networking basics, and persistent storage.
Developer tools or technical applications for simulation, experimentation, training, test and evaluation, or deployment.
Job orchestration, distributed execution, or data-processing workflows using tools such as Ray, Kubernetes Jobs, Argo Workflows, or Dagster.
Schema-driven APIs, generated clients, or service integrations using OpenAPI, JSON Schema, Protocol Buffers, gRPC, or WebSockets.
Extensible applications, plugin systems, shared component libraries, or AI-assisted workflows and agent-tool integrations.
Applications deployed across cloud, on-premises, edge, or air-gapped environments; familiarity with robotics, autonomy, or simulation.

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 useful paths to investigate failures.
Design and evolve APIs and data models that support applications, automation, and integrations with other teams.
Contribute to a platform / application architecture directly through requirements or direct contribution.
Contribute to software that is meant to run in a multitude of environments from local workstations, to CICD, to airgapped Kubernetes clusters.
Test and support what you build, using logs, metrics, traces, and user feedback to improve reliability, performance, and usability.
Work directly with users, product, design, and engineering teams to define scope, make practical tradeoffs, and deliver improvements iteratively.