Job DescriptionModern 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.
The Mission: Building the Nervous System of the SDVAt 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.
The Role: Full-Lifecycle OwnershipAs 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.
ResponsibilitiesCore 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.y requirements for diagnostic systems, including logs, metrics, traces, dashboards, alerts, and escalation workflows.
QualificationsMinimum Qualifications- Academic Foundation: BS equivalent or higher degree in Computer Science, Systems Engineering, Electrical Engineering, or a related technical field. Minimum 3.5 cumulative GPA (or equivalent evidence of technical rigor).
- Python & Automation Mastery: 1+ years of experience writing 2,000+ lines of clean, PEP8 compliant, modular Python code for data processing, API integration, or system automation.
- Production System Integration: 1+ years of experience with Git-based version control (minimum 50+ commits/merges) and containerization (Docker), including deploying at least 3 containerized applications to a cloud or local environment.
- AI/ML Implementation: 1+ years of professional/research experience in at least 2 end-to-end AI/ML projectsinvolving LLM orchestration (e.g., LangChain) or deploying a reasoning agent into a "live" state.
- Data Integrity: 1+ years of experience processing and cleaning datasets exceeding 10,000+ records for model training or inference.
- Technical Documentation: 1+ years of experience translating ambiguity into structure by authoring at least 3-5 detailed technical specifications (e.g., API contracts, System Requirements, or Sequence Diagrams).
- Problem Solving: 1+ years of experience debugging complex systems (Embedded or Cloud), resolving at least 5-10 high-priority technical blockers using Root Cause Analysis (RCA).
Even Better If You Have - Production Track Record: Experience leading at least 1 significant software module through the full lifecycle from initial requirements to a live, production environment with active users.
- Embedded Protocols: 1+ years of experience using logic analyzers or tools (Wireshark/CANoe) to decode 3+ automotive protocols (e.g., CAN, DoIP, or SOME/IP).
- Cloud Observability: Experience building real-time production dashboards in Grafana, Dynatrace, or Datadogto monitor system health and "drift."
- Systems Engineering Rigor: Experience authoring 5+ pieces of critical production documentation (FMEA, Interface Control Documents, or Production Validation Plans).
- Scale: Experience working on a system that handled 1,000+ concurrent nodes or data streams, demonstrating an understanding of horizontal scalability.
You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder...or all of the above? No matter what you choose, we offer a work life that works for you, including:
- Immediate medical, dental, vision and prescription drug coverage
- Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
- Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
- Vehicle discount program for employees and family members and management leases
- Tuition assistance
- Established and active employee resource groups
- Paid time off for individual and team community service
- A generous schedule of paid holidays, including the week between Christmas and New Year's Day
- Paid time off and the option to purchase additional vacation time.
This position is a salary grade 6 and ranges from $85,400-$143,200.
Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.
For more information on salary and benefits, click here: https://fordcareers.co/GSR
This position is hybrid. Candidates who are in commuting distance to a Ford hub location may be required to be onsite four or more days per week. #LI-Hybrid #LI-KS4
