Scientist / Research Eng General

Prognostics Research Engineer Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles. • Pioneer Physics-Informed Machine Learning (PIML): Fuse first-principles physics modeling with advanced machine learning to develop hybrid high-fidelity prognostic models that capture complex degradation behaviors across both EV and ICE powertrains. • Architect Prognostics RUL Frameworks: Design and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts. • Deploy Edge Models in C: Translate complex predictive models into highly optimized low-latency C code, bridging the gap between cloud-based data science and resource-constrained on-board vehicle electronic control units (ECUs). • Harness High-Frequency Signal Processing: Architect custom Digital Signal Processing (DSP) pipelines and time-series analytics to extract clean high-frequency physical signatures from multi-sensor vehicle networks, isolating early-stage wear patterns before they manifest as failures. • Design Multi-Sensor Fault Detection Isolation (FDI): Develop and validate intelligent multi-sensor anomaly detection frameworks capable of real-time Fault Detection and Isolation (FDI) to ensure vehicle safety system redundancy and fault-tolerant control. • Apply Statistical Causal Inference: Leverage advanced statistical methods including causal inference, multivariate analysis (ANOVA), and PCA to differentiate between mere correlation and true physical root causes of component degradation across massive connected vehicle fleets. • Own the End-to-End Pipeline (HIL to Production): Direct the entire prognostic lifecycle—moving seamlessly from mathematical conceptualization and simulation in MATLAB/Simulink to physical validation on Hardware-in-the-Loop (HIL) benches, prototype vehicles, and ultimately to production vehicle deployment. • Synthesize Deep Subsystem Domain Knowledge: Partner closely with EV and ICE component subject matter experts to translate deep physical domain knowledge (thermal, mechanical, chemical, and electrical) into robust on-board and off-board diagnostics. • Build Scale with Big Data Calibration Tools: Ingest and process large-scale telemetry data using Python, SQL, Spark, and Hadoop, while leveraging industry-standard calibration tools such as ATI and ETAS to fine-tune algorithms for real-world driving environments. • Interact with subject matter experts to understand component/system functions, leverage existing connected vehicle data to model on-board and off-board prognostics algorithms. • Operate cross-functionally to ensure successful code implementation on production vehicles. Experience Required: • Master's degree in Mechanical Engineering, Electrical Engineering, Computer Science, Computer Engineering, Physics, Mathematics, or related fields, or a combination of education and equivalent experience. • 4+ years of experience practicing statistical methods and their accurate application (e.g., ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multi-variate analysis, Neural Networks, causal inference, Gaussian regression). • 3+ years of experience with Python and related modules, SQL. • Experience with embedded controls, on-board diagnostics, sensor processing, general first principles physics modelin…