Systems Engineer

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 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.

Core Responsibilities:

• Ideation & Requirements: You will partner with cross-functional teams to define 'what' a vehicle needs to observe. You will write the technical requirements that govern how ECUs log data and how the Cloud interprets it.

• Cross-Domain Integration: You will bridge the gap between Embedded C++ firmware and Cloud-based Python microservices. You will ensure that the 'handshake' between the vehicle and the AI reasoning engine is seamless and scalable.

• AI Workflow Engineering: You will help mature the intelligent diagnostic workflows, ensuring the AI has the right 'context' (DTCs, PIDs, and logs) to perform automated root-cause analysis.

• Production Validation: You won't just hope it works; you will prove it. You will lead the system integration testing, simulating complex failures to ensure our E2E pipeline triggers the correct alerts and human-support processes.

• Fleet Observability: Once the code is in the fleet, your job continues. You will analyze real-world telemetry to refine requirements and iterate on the next generation of diagnostic capabilities.

• System Architecture: Define the specific telemetry hooks (logs, metrics, and traces) required from embedded ECUs to power cloud-based AI reasoning.

• Workflow Engineering: Build and validate the 'Diagnostic Loop',the path from a vehicle fault code (DTC) to an AI-generated repair recommendation.

• Interface Design: Define the API contracts between the vehicle's embedded gateway and the cloud-based diagnostic orchestrator.

• Performance Evaluation: Quantify the accuracy of AI diagnostic models by designing and running validation tests against known vehicle 'ground truth' data.

• The Intelligence Loop: Engineer AI-powered diagnostic capabilities that combine vehicle signals (DTCs, PIDs, Ethernet logs) with LLM-based reasoning to automate root-cause isolation.

• Observability at Scale: Define the requirements for 'Vehicle Telemetry 2.0',determining exactly what traces, metrics, and logs are needed from the embedded layer to power cloud-based dashboards and real-time alerts.

• Architectural Bridges: Work across silos to ensure that a software glitch in a Zone Controller is seamlessly captured, uploaded to the cloud, and analyzed by an AI agent to guide a technician's next-best action.

• Validation of AI Reasoning: Design frameworks to evaluate how our AI systems interpret diagnostic evidence, ensuring grounding, traceability, and 'explainability' in every repair recommendation.