Cloud Prognostics Systems EngineerOverview / Summary We are seeking a Cloud Prognostics Systems Engineer to support the development and delivery of prognostic features for connected vehicles. This role focuses on automotive systems engineering, model-based systems engineering (MBSE), vehicle data analytics, system modeling, controls, cloud-to-vehicle communications, and the development of robust prognostic solutions.
The engineer will work across system architecture, requirements, modeling and simulation, vehicle data analysis, physical system integration, safety and security, and cross-functional engineering activities.
Key Responsibilities - Apply Model-Based Systems Engineering (MBSE) methodologies using tools such as SysML and MagicDraw.
- Define system boundaries and establish logical and physical architectures.
- Map interface definitions and allocate prognostic functions across physical components.
- Use MATLAB and Simulink to design control logic, model physical system dynamics, and generate production-grade C++ code.
- Configure solver settings and manage fixed-point and floating-point data types to support effective code generation and system integration.
- Operate dynamic laboratory environments, including dyno testing and e-Daq systems.
- Select, place, and calibrate physical sensors such as accelerometers and strain gauges 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.
- Support compliance with ISO 26262, including ASIL ratings and fail-safe/fail-degraded states.
- Support compliance with ISO 21434 and secure prognostic pipelines from edge to cloud.
- Design and optimize network communication and transport protocols.
- Use Gherkin to model behavioral scenarios for cloud-to-vehicle modem communications.
- Develop dynamic data-triggering strategies and optimize payload serialization to minimize data transmission requirements.
- Use SQL on cloud platforms such as Google Cloud Platform (GCP) to partition, decode, and analyze CAN bus and sensor telemetry.
- Map raw binary logs to human-readable physical values using database-defined translation tables such as DBC or ARXML databases.
- Elicit, document, and trace multidisciplinary requirements using ALM tools such as Jama, Jira, and Team Center.
- Maintain traceability from customer experience goals through software requirements, hardware interfaces, and Design Verification Plans (DVP).
- Work with automotive communication protocols including CAN, LIN, and Automotive Ethernet.
- Integrate prognostic software applications onto central gateway modules, including management of signal routing and network timing or priority conflicts during physical system integration.
- Apply Robust Engineering principles, including creation of Parameter Diagrams (P-Diagrams), to identify system inputs, desired outputs, error states, control factors, and noise factors.
- Support algorithm tuning to improve robustness and reduce false positives.
- Connect engineering metrics such as algorithm accuracy and false-alarm rates to quality indicators including Net Promoter Score (NPS), JD Power ratings, and vehicle repair rates.
- Collaborate cross-functionally to integrate prognostic alerts into user-facing smartphone applications and support the service scheduling experience.
- Monitor, prioritize, and drive actions to improve quality metrics such as Net Promoter Score, vehicle repairs, JD Power ratings, and customer escalations.
- Investigate and resolve feature-specific issues during product development and launch.
- Analyze system failure points and evaluate solution proposals, including high-level FMA and FMEA activities.
- Lead feature development from concept through production while collecting and analyzing vehicle analytics data.
- Lead cross-functional triage activities to investigate, reproduce, and resolve complex system-level defects using simulation logs, vehicle data, and telemetry.
Required Qualifications - Master's degree.
- 5+ years of automotive experience in engineering and/or data analytics.
- 5+ years of experience with Robust Engineering fundamentals, including requirements definition, DFMEA, P-Diagrams, and validation.
- Knowledge of vehicle architecture and sensors for diagnostics and prognostics feature development.
- Experience monitoring and improving quality metrics such as Net Promoter Score, vehicle repairs, JD Power ratings, and customer escalations.
- Experience with system modeling language, including MagicDraw, and process/interface mapping.
- Experience with Atlassian Jira, Jama, and Team Center applications.
- Ability to clearly communicate technical ideas and findings to cross-functional engineering teams.
- Experience with MATLAB and Simulink modeling and C++ code generation for prognostics degradation models.
Preferred Qualifications - PhD or Master'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 experience using MATLAB/Simulink for end-to-end feature modeling and system simulation under nominal and degraded conditions.
- 2+ years of experience performing systems analysis and designing systems, subsystems, or components.
- 2+ years of experience using data-driven tools to analyze connected vehicle data or large datasets.
- 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 high-level FMAs and FMEAs.
- 2+ years of experience with cloud-to-vehicle modem communications, network communication protocols, transport protocols, and payload optimization techniques.
- Experience leading feature development from concept through production while collecting and analyzing vehicle analytics data to improve feature design and performance.
- Experience leading cross-functional triage activities to investigate, reproduce, and resolve complex system-level defects using simulation logs, vehicle data, and telemetry.
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