Join TuSimple and help change the way the world moves.
Together we’re making freight transportation safer, more efficient, and more environmentally friendly.
This opportunity is located in Tucson, AZ.
You can apply to the job with this email address: hdwe__senior_vehicle_control_engineer__integration_cba41ef32us@ivy.greenhouse.io
TuSimple Company Overview
Come join a higher calling and find a deeper purpose!
TuSimple is a fully commercialized autonomous trucking company.
As a multi-national Artificial Intelligence Technology Company, we are at the epicenter of the Autonomous Vehicle Universe.
Our breakthroughs are leading the industry in autonomous trucking.
While inventing the framework of Autonomous Driving, our live fleet of autonomous Trucks have fully evolved land freight.
TuSimple is leading the way by hiring the most talented researchers, engineers, and scientists.
TuSimple was founded half a decade ago with the goal of bringing the top minds in the world together to achieve the dream of a driverless truck solution.
With a foundation in computer vision, algorithms, mapping, and Artificial Intelligence, TuSimple has created the first commercially viable Autonomous Freight Network.
Role Responsibilities
Work across engineering teams to understand and implement requirements for vehicle control.
Develop features and functions for autonomous vehicle control systems.
Implement and develop algorithms using C++, Matlab/Simulink, and other related programming languages.
Follow ISO26262 and FMEA design processes.
Perform unit tests for a developing software.
Verify and Validate the developing software before final software integration.
Support new additions to autonomous truck system including software, firmware, and hardware.
Experience & Skills Required
MS in Automotive/Electrical/Mechanical Engineering with a focus on automotive controls or BS with relevant experiences.
Experience managing teams of 3 or more direct reports.
Solid understanding of automotive systems include: OBD II systems, brake components, tires, electrical systems, steering system, state safety inspections, engine and driveline components, etc.
Proficiency in MATLAB/Simulink/Stateflow and C++ or Python is required.
Hands on experience with automotive control systems in professional or academic projects.
Understanding and prior use of automotive communication protocols such as CAN network.
Has CAN tool experiences such as Vector CAN tool, CANalyzer, CANape, etc.
Has troubleshooting experience for complex engineering systems.
Has experiences with multimeters, oscilloscopes, etc.
Ability to work safely in a fast-paced environment.
Bonus Points
Have experience competing in student automotive competitions such as EcoCAR, Formula SAE.
Have 5+ years of industry experience in Vehicle control, Autonomous Driving and/or Robotics.
Experience in classical and modern control theory such as Model Predictive Control, Pure Pursuit, Envelope Control, etc.
Experience in Robot Operating System (ROS), ISO26262, FMEA, AUTOSAR, OSEK or BroadR-Reach.
Experience in software test (MIL/SIL/PIL/HIL, Static/Dynamic, black box/white box/grey box).
TuSimple Benefits
100% employer-paid healthcare premiums for you and your family
Work visa sponsorship available
Relocation assistance available
Breakfast, lunch, and dinner served every day
Full kitchens on every floor with unlimited snacks, drinks, special treats, fruits, meals, and more
Stock options / equity
Gym membership reimbursement
Monthly team building budget
Learning/education budget
Employer-paid life insurance
Employer-paid long and short disability
TuSimple is an Equal Opportunity Employer.
This company does not discriminate in employment and personnel practices on the basis of race, sex, age, handicap, religion, national origin, or any other basis prohibited by applicable law.
Hiring, transferring and promotion practices are performed without regard to the above-listed items.
This opportunity is located in Tucson, AZ.
You can apply to the job with this email address: hdwe__senior_vehicle_control_engineer__integration_cba41ef32us@ivy.greenhouse.io
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