Systems Engineer

Ford Ford · Auto · Dearborn, MI +1 · PD Operations and Quality

Systems Engineer role focused on building AI-powered Embedded Vehicle Diagnostics capabilities. This involves integrating vehicle signals, diagnostics, logs, and engineering knowledge with AI/ML engineering to improve fault isolation, guide actions, and support escalations. The role bridges embedded systems, cloud services, diagnostics, and AI/ML, owning the full lifecycle from vehicle silicon to cloud neural networks. Responsibilities include defining requirements for data logging, integrating C++ firmware with Python microservices, maturing AI diagnostic workflows, production validation, fleet observability, system architecture, and evaluating AI reasoning.

What you'd actually do

  1. Define the specific telemetry hooks (logs, metrics, and traces) required from embedded ECUs to power cloud-based AI reasoning.
  2. Build and validate the "Diagnostic Loop"—the path from a vehicle fault code (DTC) to an AI-generated repair recommendation.
  3. Define the API contracts between the vehicle's embedded gateway and the cloud-based diagnostic orchestrator.
  4. Quantify the accuracy of AI diagnostic models by designing and running validation tests against known vehicle "ground truth" data.
  5. Engineer AI-powered diagnostic capabilities that combine vehicle signals (DTCs, PIDs, Ethernet logs) with LLM-based reasoning to automate root-cause isolation.

Skills

Required

  • BS equivalent or higher degree in Computer Science, Systems Engineering, Electrical Engineering, or a related technical field.
  • Minimum 3.5 cumulative GPA (or equivalent e

Nice to have

  • Embedded C++ firmware
  • Cloud-based Python microservices
  • AI/ML engineering
  • Diagnostics
  • Observability
  • Telemetry
  • LLM-based reasoning

What the JD emphasized

  • AI-powered Embedded Vehicle Diagnostics
  • AI/ML Engineering
  • AI reasoning engines
  • LLM-based reasoning

Other signals

  • AI-powered Embedded Vehicle Diagnostics
  • intelligent pipeline that integrates embedded telemetry, cloud-based data lakes, and AI reasoning engines
  • AI/ML Engineering
  • AI-enabled diagnostics for next-generation vehicles
  • AI reasoning engines
  • AI/ML Engineering
  • intelligent diagnostic workflows
  • AI has the right "context" (DTCs, PIDs, and logs) to perform automated root-cause analysis
  • AI diagnostic models
  • AI-powered diagnostic capabilities that combine vehicle signals (DTCs, PIDs, Ethernet logs) with LLM-based reasoning to automate root-cause isolation
  • AI agent to guide a technician’s next-best action
  • evaluate how our AI systems interpret diagnostic evidence