Full Stack Software Engineer
US$85,500 – US$166,500 / yr base salary
Posted 9 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 1–4 yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Visa sponsorship
- Not offered
| Base salary | US$85,500 – US$166,500 / yr |
|---|
In this position....
Ford's connected vehicles send us a constant stream of data: trips, alerts, battery and health signals, and much more. The Connected Vehicle Data Team in GDIA (Global Data Insight & Analytics) builds the services, APIs and applications that make that data easy to find, understand, and use across Ford.
We're looking for an early- to mid-career full-stack software engineer who likes building things end to end: backend services in Java and Spring Boot, frontends in React or Angular, and the cloud deployments and data flows that connect them. You'll deploy to OpenShift and Google Cloud, learn how streaming data behaves at very large scale, and see your work used to spot vehicle issues sooner.
You don't need to have done everything on this page. We care more that you're curious, careful with details, and eager to learn. That's what the Ford OS Behaviors are about: be a builder, play to win, and deliver the promise.
Responsibilities
What you’ll do….
- Backend services and APIs. Build and maintain REST APIs and microservices in Java and Spring Boot, backed by SQL databases and BigQuery.
- Frontend apps and dashboards. Build features and reusable components in React or Angular with TypeScript that help people explore vehicle data.
- Streaming integrations. Write Kafka and Pub/Sub producers and consumers, and learn how to handle retries, failed messages, and message order.
- Cloud deployments. Package apps with Docker and deploy them to OpenShift and GCP (Cloud Run, GKE), including configuration, secrets, and health checks.
- Testing and quality. Write unit and integration tests with JUnit and frontend test tools (for example, Jest or Cypress), take part in code reviews and pairing, and learn test-driven development.
- Monitoring and support. Add logging, alerts, and dashboards in Cloud Logging and Cloud Monitoring, and help troubleshoot production issues using runbooks.
- Good engineering habits. Use Git and CI/CD (GitHub Actions, Tekton), keep dependencies up to date, fix issues found by code quality and security scans, and follow secure coding, API security, and data privacy practices.
- AI in practice. Use tools like GitHub Copilot every day for code, tests, and documentation. You'll also help with the team's AI work, for example:
- building the API and UI for an assistant that answers plain-English questions about vehicle data
- calling LLM APIs to draft metadata, summaries, or release notes