Algorithm Development & Modeling
Anomaly Detection: Design and implement GPU-accelerated machine learning models (e.g., XGBoost, autoencoders, and GANs) to identify irregular patterns in high-frequency sensor data.
Digital Twin Engineering: Partner with engineering teams to develop onboard digital twins using NVIDIA architecture to simulate, predict, and optimize the performance of heavy machinery
Optimization: Profile and tune deep learning algorithms for maximum efficiency on NVIDIA GPU architectures, ensuring high throughput and low latency for real-time monitoring.
Testing onboard Architecture & Integration
Edge Deployment: Adapt and test algorithms for onboard architecture, leveraging tools like NVIDIA Jetson and real-time edge processing on Cat equipment.
Hardware-Software Co-Design: Collaborate with hardware / simulation engineers to ensure algorithm compatibility with next-generation processors and specialized onboard compute modules.
Simulation-Based Training: Use high-fidelity digital twins to simulate rare failure scenarios, ensuring the GenAI assistant provides accurate troubleshooting steps for edge-case mechanical issues.
GenAI Algorithm Automated Diagnostic Workflows: Develop Generative AI agents that synthesize telematics data to generate prioritized repairs for identified machine faults.
Unified Data Orchestration: Integrate multi-modal outputs from condition monitoring analytics & asset life history to create a machine-specific context for AI assistant.

Requirements

Typically, a Bachelors, Masters, or PhD degree in Applied Statistics, Data Science, Business Analytics, Predictive Analytics, Business Intelligence & Analytics, Mathematics, Computer Science, Engineering (Aerospace, Electrical, Mechanical, Computer, Industrial, Agricultural, etc.), or equivalent technical degree
Extensive experience applying Python (NumPy, SciPy, pandas, etc.) programming to solve business challenges.
Extensive experience with advanced data analysis, machine learning such as clustering, Log regressions, neural nets, and statistical methods such as statistical process control, etc. (typically 8+ years)
Experience in practical applications of onboard architecture / software (e.g. mini projects using Raspberry Pi or any other architecture is a bonus)
Working experience with heavy equipment engineering or data analysis.
Working knowledge with cloud technologies (AWS, Azure, Google Cloud, etc.)
Advanced experience with version control / repositories such as GitHub
Experience operating in an Agile environment
Must demonstrate strong initiative, interpersonal skills, and the ability to communicate effectively.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

algorithm developmentanomaly detectionmachine learningXGBoostautoencodersGANsdeep learningdata analysisPythonstatistical methods

Soft Skills

initiativeinterpersonal skillscommunication

Certifications

Bachelors degreeMasters degreePhD degree