Research Engineer / Scientist

About the role

Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles. Pioneer Physics-Informed Machine Learning (PIML) by fusing 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 to design and deploy state-of-the-art prognostics models that accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts. Deploy Edge Models in C++ to 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 to 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) to 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 to 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) 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 by partnering 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 by ingesting and processing 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.

Responsibilities

• Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles.
• Pioneer Physics-Informed Machine Learning (PIML) by fusing 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 to design and deploy state-of-the-art prognostics models that accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts.
• Deploy Edge Models in C++ to 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 to 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) to 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 to 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) 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 by partnering 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 by ingesting and processing 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.

Requirements

• Master's in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience.
• 4+ years of experience of practicing statistical methods and their accurate application e.g. ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multivariate analysis, Neural Networks, causal inference, Gaussian regression, etc.
• 3+ Experience with Python (and related modules), SQL
• Experience with embedded controls, onboard Diagnostic, Sensor Processing, General First Principles Physics Modeling and simulation using numerical computational tool (e.g. MATLAB, ATI, Simulink)
• Experience with Digital Signal Processing (DSP) data structures, algorithms, and software engineering principles
• Self-motivated, strong analytical, excellent interpersonal and communication skills required

Skills

• C++, ALGORITHMS, Data Science, Google Cloud Platform, Python, SQL, MATLAB modeling

Qualifications

• PhD in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience.
• Experience in Dynamic Systems, Control, Robotics, Prognostics and Health Management.
• Familiarity working with Automotive prognostics feature development using connected vehicle data.
• 2+ Experience in application of statistical and machine learning methods e.g., ANOVA, PCA, clustering methods, causal inference, time series forecasting, random forest, multi-variate analysis, neural networks, etc.
• Expertise in open-source data science technologies such as Python, R, Spark, Hadoop, etc. acquired through college course work, online training and certification or project development.
• Experience in software development for automotive controls with hands on experience using MATLAB for large scale data and understanding of programming fundamentals and experience with C++ programming in embedded environments.
• ATI and ETAS calibration tool familiarity

Benefits

• Hybrid / 4 days per week in the office

Pay

TBD

Schedule

TBD