Autonomous Vehicle Modeling & ML Data Engineer

42dot Inc.; Sunnyvale (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

42dot Inc. is seeking a Machine Learning & Data Engineer, Vehicle Modeling, to develop ML and data infrastructure that improves vehicle models and supports broader intelligence and autonomous driving development.

You’ll work across vehicle telemetry, simulation, test data, fleet data, and cloud platforms to build scalable systems for model development, evaluation, and continuous improvement. This role sits at the intersection of ML, data engineering, and physical system modeling.

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

Skills

Machine Learning

Data Engineering

Hybrid Physics-Data

Model Calibration

Parameter Estimation

Cloud Platforms

Job description

42dot Inc. is seeking a Machine Learning & Data Engineer, Vehicle Modeling, to develop ML and data infrastructure that improves vehicle models and supports broader intelligence and autonomous driving development.

You’ll work across vehicle telemetry, simulation, test data, fleet data, and cloud platforms to build scalable systems for model development, evaluation, and continuous improvement. This role sits at the intersection of ML, data engineering, and physical system modeling.