Machine Learning & Data Engineer, Vehicle Modeling

42dot
Sunnyvale, San Francisco
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningSkills: ["Reproducibility","Data analysis","Problem-solving","Cross-functional collaboration"]

Develop ML and data infrastructure to improve physics-based vehicle models, using real-world telemetry, simulation, and test data. Build scalable pipelines for ingesting, cleaning, synchronizing, and evaluating large vehicle time-series datasets. Create cloud-based systems for data processing, model training, simulation, validation, and experiment tracking, with tools that enable reproducible model development and continuous model improvement for autonomous driving workflows.

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FursaFursa
42dot
42dot
4 days ago

Machine Learning & Data Engineer, Vehicle Modeling

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Source: Company careers pageValidated by: Fursa AI
Last checked: 12 hours agoStatus: Live

Job Summary

Develop ML and data infrastructure to improve physics-based vehicle models, using real-world telemetry, simulation, and test data. Build scalable pipelines for ingesting, cleaning, synchronizing, and evaluating large vehicle time-series datasets. Create cloud-based systems for data processing, model training, simulation, validation, and experiment tracking, with tools that enable reproducible model development and continuous model improvement for autonomous driving workflows.
Location: Sunnyvale, San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key 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 to identify 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 and tooling for data processing, model training, simulation, evaluation, validation, and reproducible model development.

Key Requirements

  • •Develop machine learning and hybrid physics-data approaches to improve vehicle and component model accuracy.
  • •Apply model calibration, parameter estimation, adaptive modeling, and data-driven model improvement across different vehicle states and operating conditions.
  • •Build workflows for comparing model predictions with test, simulation, and real-world vehicle data to identify opportunities for 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.
Experience:Mobility AIAutonomous drivingVehicle telemetrySimulationTime-series data
Skills:ReproducibilityData analysisProblem-solvingCross-functional collaboration
Tech Stack:Machine learningPhysics-based modelsHybrid physics-and-data approachesSimulationVehicle telemetryTime-series dataCloud platformsDataset managementModel evaluationExperiment tracking

Company Brief

42dot
Develops autonomous driving and mobility software platforms, including AI-based perception, mapping, routing, and connected-vehicle technologies. It works on next-generation transportation systems and self-driving vehicle capabilities for automotive applications.
Industry: Autonomous Vehicles
Company Size: Large (251 to 1,000 employees)
Growth: Scaleup
Headquarters: Seoul, South Korea
Founded: 2019
WebsiteLinkedIn