Systems Engineer

Ford • Dearborn, MI

5d • Hybrid

About The Position

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.

Requirements

• 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).
• 1+ years of experience writing 2,000+ lines of clean, PEP8 compliant, modular Python code for data processing, API integration, or system automation.
• 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.
• 1+ years of professional/research experience in at least 2 end-to-end AI/ML projects involving LLM orchestration (e.g., LangChain) or deploying a reasoning agent into a "live" state.
• 1+ years of experience processing and cleaning datasets exceeding 10,000+ records for model training or inference.
• 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).
• 1+ years of experience debugging complex systems (Embedded or Cloud), resolving at least 5-10 high-priority technical blockers using Root Cause Analysis (RCA).

Nice To Haves

• Experience leading at least 1 significant software module through the full lifecycle from initial requirements to a live, production environment with active users.
• 1+ years of experience using logic analyzers or tools (Wireshark/CANoe) to decode 3+ automotive protocols (e.g., CAN, DoIP, or SOME/IP).
• Experience building real-time production dashboards in Grafana, Dynatrace, or Datadog to monitor system health and "drift."
• Experience authoring 5+ pieces of critical production documentation (FMEA, Interface Control Documents, or Production Validation Plans).
• Experience working on a system that handled 1,000+ concurrent nodes or data streams, demonstrating an understanding of horizontal scalability.

Responsibilities

• Ideation & Requirements: Partner with cross-functional teams to define "what" a vehicle needs to observe. Write the technical requirements that govern how ECUs log data and how the Cloud interprets it.
• Cross-Domain Integration: Bridge the gap between Embedded C++ firmware and Cloud-based Python microservices. Ensure that the "handshake" between the vehicle and the AI reasoning engine is seamless and scalable.
• AI Workflow Engineering: Mature the intelligent diagnostic workflows, ensuring the AI has the right "context" (DTCs, PIDs, and logs) to perform automated root-cause analysis.
• Production Validation: Lead the system integration testing, simulating complex failures to ensure our E2E pipeline triggers the correct alerts and human-support processes.
• Fleet Observability: 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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree