Systems Engineer Controls

V2Soft•Dearborn, MI

3d•Hybrid

About The Position

V2Soft is seeking a Cloud Prognostics Engineer (Systems Engineer) to join their team. This role involves utilizing MBSE methodologies with tools like SysML and MagicDraw to define system boundaries, establish architectures, map interfaces, and allocate prognostic functions. The engineer will use MATLAB and Simulink to design control logic, model system dynamics, and auto-generate C++ code, ensuring seamless integration into automotive operating systems. Hands-on experience with dynamic laboratory environments, including dyno testing and e-Daq systems, is required for sensor selection, calibration, data capture, and preparation for analysis. The role also demands deep expertise in safety and security assessments (FMEA, FMEDA, cybersecurity threat modeling) and designing systems to comply with ISO 26262 and ISO 21434. Additionally, the engineer will design optimized network communication and transport protocols, use Gherkin for behavioral scenario modeling, and design dynamic data-triggering strategies. Proficiency in SQL on cloud platforms like GCP for data analysis and mapping raw logs to human-readable values is essential. Advanced capability in eliciting, documenting, and tracing requirements using ALM tools like Jama, Jira, and Team Center is also required. In-depth knowledge of automotive communication protocols (CAN, LIN, Automotive Ethernet) and integrating prognostic software onto central gateway modules is necessary. Practical application of Robust Engineering principles, including P-Diagrams, is expected to ensure algorithm robustness. The engineer will connect technical metrics to real-world quality indicators and collaborate with divisions like the Ford Customer Service Division (FCSD) to integrate prognostic alerts into user-friendly smartphone applications.

Requirements

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.)
Ability to clearly communicate technical ideas/findings to cross functional engineering teams.
C++
MATLAB modeling

Nice To Haves

Phd or Masters 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) and utilizing MATLAB/Simulink for end-to-end feature modeling and system simulation to ensure robustness under nominal and degraded conditions.
2+ years of experience performing systems analysis and designing systems, subsystems, or components defining requirements as described above.
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.

Responsibilities

Define system boundaries, establish logical and physical architectures, map interface definitions, and allocate prognostic functions across different physical components.
Use MATLAB and Simulink to design control logic, model physical system dynamics, and auto-generate production-grade, highly efficient C++ code.
Configure solver settings, manage data types (fixed-point vs. floating-point), and ensure generated code integrates seamlessly 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 (determining ASIL ratings and designing fail-safe/fail-degraded states) and ISO 21434.
Design optimized network communication and transport protocols.
Use Gherkin to model behavioral scenarios of cloud-to-vehicle modem communications.
Design dynamic data-triggering strategies (e.g., only uploading detailed vibration spectra when an anomaly threshold is crossed).
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 (such as DBC or ARXML databases).
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 (such as Ford’s Rigil/Enhanced central gateway).
Manage signal routing, and resolve network timing or priority conflicts during physical system integration.
Apply Robust Engineering principles, specifically creating Parameter Diagrams (P-Diagrams) to identify system inputs, desired outputs, error states, control factors, and noise factors.
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.
Collaborate cross-functionally with divisions like the Ford Customer Service Division (FCSD) to integrate prognostic alerts into user-friendly smartphone applications.