What are the responsibilities and job description for the System Engineer Controls position at XA.net?
Position Description:
Cloud Prognostics Engineer (Systems Engineer) 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 (Google Cloud Platform) 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.
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.
Skills Required:
C , MATLAB modeling 1. MATLAB and C - Ability to model using MATLAB Simulink and generate C code for our prognostics degradation models.
Experience Required:
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.
Education Required:
Master's Degree
***HYBRID / 4 days per week in the office***
Cloud Prognostics Engineer (Systems Engineer) 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 (Google Cloud Platform) 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.
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.
Skills Required:
C , MATLAB modeling 1. MATLAB and C - Ability to model using MATLAB Simulink and generate C code for our prognostics degradation models.
Experience Required:
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.
Education Required:
Master's Degree
***HYBRID / 4 days per week in the office***