Cloud Prognostics Engineer

Ford Motor • Dearborn, MI

32d • Hybrid

About The Position

Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You’ll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out. Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams. Responsibilities In this position... Are you passionate about leveraging modern day methodologies/tools to understand automotive systems, study and predict the degradation or occurrence of a problem in a vehicle component/system? Would you love to accelerate our efforts to build amazing experiences and software products in the Connected Vehicles space - with data? We are seeking a top-tier Cloud Prognostics Engineering professional who is data driven, self-motivated and detail oriented to help develop and deliver breakthrough Prognostic Features. What you'll do... MBSE methodologies using tools like SysML and MagicDraw. The engineer must be capable of defining system boundaries, establishing logical and physical architectures, mapping interface definitions, and allocating prognostic functions across different physical components (e.g., deciding which calculations run on a local sensor, the central gateway, or the cloud). Using MATLAB and Simulink to design control logic, model physical system dynamics, and auto-generate production-grade, highly efficient C++ code. The engineer must understand how to configure solver settings, manage data types (fixed-point vs. floating-point), and ensure the generated code integrates seamlessly into automotive operating systems. Hands-on experience operating dynamic laboratory environments, including dyno testing and e-Daq systems. The engineer must know how to select, place, and calibrate physical sensors (like accelerometers and strain gauges) on prototype vehicles, capture high-fidelity physical data, and prepare those datasets for algorithmic analysis. Deep expertise in performing safety and security assessments, including FMEA (Failure Mode and Effects Analysis), FMEDA, and cybersecurity threat modeling. The engineer must design the system to comply with ISO 26262 (determining ASIL ratings and designing fail-safe/fail-degraded states) and ISO 21434 to ensure the prognostic pipeline is secure from edge to cloud. Designing optimized network communication and transport protocols. This includes using Gherkin to model behavioral scenarios of cloud-to-vehicle modem communications, designing dynamic data-triggering strategies (e.g., only uploading detailed vibration spectra when an anomaly threshold is crossed), and optimizing payload serialization to minimize data transmission costs. Strong proficiency in using SQL on cloud platforms like Google Cloud Platform (GCP) to partition, decode, and analyze raw CAN bus and sensor telemetry. The engineer must know how to map raw binary hex logs back to human-readable physical values using database-defined translation tables (such as DBC or ARXML databases). Advanced capability in eliciting, documenting, and tracing complex, multi-disciplinary requirements using Application Lifecycle Management (ALM) tools like Jama, Jira, and Team Center. The engineer must ensure seamless traceability from high-level customer experience goals down to software requirements, hardware interfaces, and Design Verification Plans (DVP). In-depth knowledge of automotive communication protocols, including CAN, LIN, and Automotive Ethernet. The engineer must be highly skilled in integrating prognostic software applications onto central gateway modules (such as Ford’s Rigil/Enhanced central gateway), managing signal routing, and resolving network timing or priority conflicts during physical system integration. Practical application of Robust Engineering principles, specifically creating Parameter Diagrams (P-Diagrams) to identify system inputs, desired outputs, error states, control factors (design parameters), and noise factors (environmental, wear, manufacturing tolerances). This ensures the algorithm is tuned to be highly robust against false positives. Ability to connect technical engineering metrics (such as algorithm accuracy, false-alarm rates) to real-world quality indicators like Net Promoter Score (NPS), JD Power ratings, and Vehicle Repair rates. The engineer must collaborate cross-functionally with divisions like the Ford Customer Service Division (FCSD) to integrate prognostic alerts into user-friendly smartphone applications, ensuring a seamless, anxiety-free service scheduling experience for Ford and Lincoln owners.

Requirements

• Bachelor’s degree in Automotive Engineering, Systems Engineering, Mechanical Engineering, Electrical Engineering, Electronics Engineering, Computer Science, or a related field.
• 2+ years of experience defining system requirements using Gherkin scenarios (Given-When-Then).
• 2+ years of utilizing MATLAB/Simulink for end-to-end feature modeling and system simulation.
• 2+ years of experience performing systems analysis and designing systems, subsystems, or components.
• 2+ years of leveraging Data-Driven tools to analyze Connected Vehicle Data or large data sets.
• 2+ years of experience investigating and resolving feature-specific issues during product development and launch, or analyzing system failure points and evaluating solution proposals including conducting high level FMAs and FMEAs.
• 2+ years of experience interfacing cloud-to-vehicle modem communications, or applying network communication protocols, transport protocols, and payload optimization techniques.
• Experience in leading the development of a feature from concept to production while collecting and analyzing vehicle analytics data to improve feature design and performance.
• Experience leading cross-functional triaging activities to systematically investigate, reproduce, and resolve complex system-level defects by analyzing simulation logs, vehicle data, and telemetry.
• Ability to clearly communicate technical ideas/findings to cross functional engineering teams.

Nice To Haves

• Master’s degree with 5+ years of automotive experience in engineering and/or data analytics.
• 5+ years of proven knowledge of Robust Engineering Fundamentals including defining requirements, DFMEA, P-Diagrams and validation.
• Knowledge of vehicle architecture and sensors for diagnostics and prognostics feature development.
• Monitor, prioritize and drive actions to improve on traditional Quality Metrics: Net Promoter Score, Vehicle Repairs, JD Power, Customer Escalations to improve feature performance.
• Experience working with system modeling language (MagicDraw) and process/interface mapping.
• Experience working with Atlassian JIRA, JAMA and Team Center applications (VSEM, etc.).

Responsibilities

• Define system boundaries, establish logical and physical architectures, map interface definitions, and allocate prognostic functions across different physical components.
• Design control logic, model physical system dynamics, and auto-generate production-grade, highly efficient C++ code using MATLAB and Simulink.
• Configure solver settings, manage data types (fixed-point vs. floating-point), and ensure seamless integration of generated code into automotive operating systems.
• Operate dynamic laboratory environments, including dyno testing and e-Daq systems.
• Select, place, and calibrate physical sensors on prototype vehicles, capture high-fidelity physical data, and prepare datasets for algorithmic analysis.
• Perform safety and security assessments, including FMEA, FMEDA, and cybersecurity threat modeling.
• Design systems to comply with ISO 26262 and ISO 21434.
• Design optimized network communication and transport protocols, including modeling behavioral scenarios and designing dynamic data-triggering strategies.
• Optimize payload serialization to minimize data transmission costs.
• Use SQL on cloud platforms like Google Cloud Platform (GCP) to partition, decode, and analyze raw CAN bus and sensor telemetry.
• Map raw binary hex logs back to human-readable physical values using database-defined translation tables.
• Elicit, document, and trace complex, multi-disciplinary requirements using Application Lifecycle Management (ALM) tools like Jama, Jira, and Team Center.
• Ensure seamless traceability from high-level customer experience goals down to software requirements, hardware interfaces, and Design Verification Plans (DVP).
• Integrate prognostic software applications onto central gateway modules, manage signal routing, and resolve network timing or priority conflicts.
• Apply Robust Engineering principles, creating Parameter Diagrams (P-Diagrams) to identify system inputs, desired outputs, error states, control factors, and noise factors.
• Connect technical engineering metrics to real-world quality indicators like Net Promoter Score (NPS), JD Power ratings, and Vehicle Repair rates.
• Collaborate cross-functionally to integrate prognostic alerts into user-friendly smartphone applications.

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