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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Systems Engineer - **Company:** Ford Motor Company - **Location:** Dearborn, MI, United States - **Salary:** $99,600.0 - $166,600.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Software Applications, Systems Engineering, Artificial Neural Networks, Microsoft Azure, Cloud Computing, Cloud Engineering, Computer Engineering, Data Visualization, Relational Databases, Database Connection, Database Design, Web Development, Ethernet, Failure Mode Effects Analysis, Mobile Application Software, PostgreSQL, Machine Learning, Operational Data Store, Cloud Services, Simulink, Software Deployment, Software Engineering, SQL Databases, System Integration Testing, System Testing, Systems Integration, TypeScript, Data Logging, Data Processing, Scripting, ReactJS, Retrieval-Augmented Generation, Large Language Models, Software Application Programming, Caching, Generative AI, Build Management, Information Technology, HMI Screens, Cloudflare, Graphql, React Native, Restful APIs, Software Usability, Api Management - **Published:** October 15, 2026 - **Apply:** https://www.careerjet.com/job/us96dffde56af9b9e85d651a85abeb00f7/eaa ## About the Role BS or equivalent degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience developing and deploying software solutions. 1+ years of professional or co-op experience developing web applications using React, JavaScript/TypeScript, or comparable technologies, including experience with API integration, relational databases, and production deployment. Experience developing and modifying Python scripts to automate engineering workflows, improve software build processes, or streamline data processing. Ability to identify redundant steps and deliver measurable efficiency improvements. Experience delivering at least 1 application from requirements and database design through deployment and user adoption. Familiarity with GraphQL or REST APIs, SQL, and database access tools such as Prisma or comparable frameworks. Experience building at least 1 AI-enabled application using LLM APIs, retrieval-augmented generation (RAG), or conversational agents. Ability to integrate internal knowledge sources or external information services into practical user-facing workflows. Experience integrating machine, equipment, or operational data into software applications, including dashboards or automated workflows. Understanding of manufacturing metrics such as availability, performance, quality, cycle time, or overall equipment effectiveness (OEE). Experience creating or improving technical documentation, user manuals, demonstration materials, or application requirements. Ability to work with engineers, customers, and operational stakeholders to translate needs into usable software. Experience diagnosing software or hardware issues, identifying root causes, and implementing corrective actions. Demonstrated ability to improve system usability, processing efficiency, or engineering productivity. Even better, you may have... Experience independently owning a significant software application or module through the full lifecycle, including requirements gathering, architecture, implementation, deployment, and support for active users. Experience developing applications within a manufacturing execution system (MES), particularly for quality, changeover, preventive maintenance, tooling management, or production analytics. Experience building preventive maintenance or asset-management workflows involving work orders, maintenance history, reusable task libraries, and time- or usage-based scheduling. Cloud & Scheduled Processing: Experience using Azure, Cloudflare Workers, or comparable cloud services to support scheduled processing, API integrations, or application backends. Experience deploying a RAG-based troubleshooting assistant that retrieves relevant guidance from historical downtime records, escalation logs, or internal technical documentation. Experience supporting vehicle or embedded-system testing using Simulink, Raptor-CAL, Raptor-DEV, or comparable development and calibration tools, including HMI development. Experience building production dashboards that combine live machine APIs, production targets, and historical shift data to help engineers and supervisors investigate performance losses. Experience developing mobile applications using React Native/Expo, Supabase, or comparable technologies, including authentication, API integration, caching, state management, and local persistence. A track record of delivering software improvements that reduce recurring costs, shorten engineering workflows, improve customer support, or replace manual processes and third-party tools. ## Description As a Systems Engineer - End-to-End Software Diagnostics & Observability within the Electric Vehicles, Digital and Design (EVDD) team, you will not be a "siloed" contributor. You will sit at the epicenter of Embedded Systems, Cloud Architecture, and AI/ML Engineering, owning the entire birth-to-deployment journey of intelligent diagnostic workflows. You will be the architect of the data's journey-from the vehicle's silicon to the cloud's neural networks-ensuring that our systems are production-hardened, scalable, and serve a diverse global ecosystem of remote users, 3rd-party technicians, and enterprise stakeholders. What you'll do... Cradle-to-Grave Feature Ownership: Partner with cross-functional teams to define "what" a vehicle needs to observe, write the technical requirements (ECU logging/Cloud interpretation), and lead the integration through to global production monitoring. The Quality Gatekeeper: Define and enforce the "Definition of Done" for diagnostic workflows. You are essential to ensuring that code is not only functional but observable, maintainable, and meets Ford's rigorous production benchmarks. Engineering for Personas: Tailor system behavior and data visualization for a diverse user base. Ensure a 3rd-party technician gets a "repair hint," a remote driver experiences a seamless fix, and an enterprise engineer receives high-fidelity raw telemetry. Technical-to-Business Translation: Distill complex system telemetry into actionable insights. You must be able to communicate technical tradeoffs and root-cause analyses clearly to both deep-tech engineering teams and non-technical leadership. The Intelligence Loop: Engineer AI-powered diagnostic capabilities that combine vehicle signals (DTCs, PIDs, Ethernet logs) with LLM-based reasoning (RAG) to automate root-cause isolation. Production Validation: Lead system integration testing and simulate complex failure modes (FMEA) to ensure our E2E pipeline triggers the correct alerts and human-in-the-loop support processes.