What you'll do
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.
- Key outcomes:
- A successful engineer in this role will help deliver the following outcomes:
Hivemind teams can complete development and evaluation workflows through coherent applications and dependable services.
Features work reliably across supported local and cluster environments, with clear diagnostics when something goes wrong.
Other teams can integrate with and extend Forge through documented APIs and reusable components.
- Changes are straightforward to test, deploy, maintain, and support as the platform evolves.
- Required qualifications:
- 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.
- Preferred qualifications:
- Experience in every area below is not expected. Relevant experience may include:
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.
- Why join us:
- Own complete solutions. Shape the user experience, implement the services behind it, and see your work through deployment and real use.
Build software with direct mission impact. Help engineers develop, validate, and deliver autonomy capabilities more effectively.
Work alongside engaged users. Partner with autonomy, ML, and simulation experts and see how your decisions affect their daily work.
Influence a growing platform. Turn practical application needs into shared capabilities that benefit teams across Hivemind, including new AI-assisted engineering workflows.
