Cloud Prognostics Engineer

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.

We made history and now we work to transform the future - for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

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 Magic Draw. 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…