Systems Engineer – End-to-End Software Diagnostics & Observability

At Ford Motor Company, we believe freedom of movement drives human progress. With our exciting plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation. 

You’ll join an agile team of doers pioneering our EV future. We are customer-obsessed, entrepreneurial, and data-driven. 

Modern vehicles are increasingly software-defined, connected, and intelligent. Delivering a best-in-class ownership and service experience now depends on Ford’s ability to detect, understand, diagnose, and resolve complex software and electronics issues quickly and accurately. That is why Ford is investing in an End-to-End Software Diagnostics & Observability initiative focused on transforming how vehicle issues are understood across engineering, diagnostics, and service workflows. 

We are building state-of-the-art AI-powered Embedded Vehicle Diagnostics capabilities that combine vehicle signals, diagnostics, logs, engineering knowledge, service procedures, and intelligent reasoning to improve case quality, accelerate fault isolation, guide next-best actions, and support scalable human-in-the-loop escalation. This initiative sits at the intersection of embedded systems, cloud services, diagnostics, observability, and AI/ML engineering. 

Do you want to help define the future of AI-enabled diagnostics for next-generation vehicles? Ford’s team is a fast-paced, highly collaborative organization that translates advanced technical strategy into deployable capabilities. If you are passionate about AI/ML, complex systems, embedded software, and solving real-world engineering problems at scale, consider joining our forward-thinking team. 

At Ford Motor Company, we believe freedom of movement drives human progress. As vehicles become software-defined, intelligent, and connected, our ability to compete depends on a fundamental shift: moving from reactive diagnostics to proactive observability.

We are overhauling our global legacy systems to build a state-of-the-art End-to-End (E2E) Software Diagnostics & Observability platform. This is the "nervous system" for our next generation of vehicles—an intelligent pipeline that integrates embedded telemetry, cloud-based data lakes, and AI reasoning engines to resolve complex issues before they impact the customer.

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.

• 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.