Vehicle Test Driver - Michigan

Applus IDIADAAnn Arbor, MI
Onsite

About The Position

Applus+ IDIADA is seeking Driver Operators to join our team. The ideal candidate is technically adept and possesses general knowledge of the automotive industry and its products. The driver would be responsible for gathering data to customer specifications across North America, and documenting observations in a clear and detailed manner through our Data Acquisition system. This project is expected to last 4-5 months with consecutive projects thereafter. Applus+ IDIADA is a global leader in automotive engineering, offering a dynamic and rewarding career opportunity for professionals passionate about shaping the future of mobility. As a TOP Employer certified company with over 3400 professionals in 24 countries, we provide a diverse and inclusive work environment that fosters innovation and growth with colleagues from over 57 nationalities contributing to a safer, more efficient, and sustainable vehicles.

Requirements

  • High School diploma
  • 5 years of licensed driving experience
  • Must have current Driver's License and a clean driving record for a minimum of 3 years.
  • We are a drug free work environment
  • Clean driving record

Nice To Haves

  • Experience working with ADAS features highly preferred.

Responsibilities

  • Carry out various driving services on vehicles in line with customer specifications.
  • Drive and operate vehicles across a pre-defined routes through various terrain and weather conditions.
  • Perform predatory activities before shift, including but not limited to inspection of the vehicle before and after shift completion.
  • Manage data acquisition systems to ensure accurate data retrieval.
  • Drive vehicles in a safe manner and following traffic regulations.
  • Follow instructions provided by project manager.
  • Perform reporting activities daily and when required by the program manager.
  • Maintain all documentation with utmost confidentiality.
  • Support the acquisition of data and implement proper tagging techniques to document objects, obstacles, and anomalies found during data acquisition.
© 2026 Teal Labs, Inc
Privacy PolicyTerms of Service