ML & Data Engineer, Vehicle Modeling (Hybrid Physics-ML)

AI Chopping Block; Sunnyvale (CA); Hybrid; USD 140,000 - 180,000; Full time

Job summary

42dot is hiring a Machine Learning & Data Engineer, Vehicle Modeling, to develop ML and data infrastructure that improves vehicle models and supports autonomous driving development. You will work across telemetry, simulation, test data, fleet data, and cloud platforms to build scalable systems for model development and evaluation.

A core focus is using real-world vehicle data to identify model gaps and improve models through calibration, hybrid physics-and-data methods, and parameter estimation

Responsibilities

• Develop machine learning and hybrid physics-data approaches to improve vehicle and component model accuracy.
• Develop methods for model calibration, parameter estimation, adaptive modeling, and data-driven model improvement across different vehicle states and operating conditions.
• Build scalable workflows for comparing model predictions with test, simulation, and real-world vehicle data and identifying opportunities for model improvement.
• Build pipelines for ingesting, cleaning, synchronizing, transforming, storing, and accessing large-scale vehicle telemetry and time-series data.
• Develop cloud-based infrastructure for data processing, model training, simulation, evaluation, and validation.
• Build tools and workflows for dataset management, model evaluation, experiment tracking, and reproducible model development.
• Support data processing, model evaluation, and analysis workflows used by vehicle intelligence

Job description

42dot is hiring a Machine Learning & Data Engineer, Vehicle Modeling, to develop ML and data infrastructure that improves vehicle models and supports autonomous driving development. You will work across telemetry, simulation, test data, fleet data, and cloud platforms to build scalable systems for model development and evaluation.

A core focus is using real-world vehicle data to identify model gaps and improve models through calibration, hybrid physics-and-data methods, and parameter estimation