TuSimple was founded in 2015 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 AI, TuSimple is working to create the first commercially viable autonomous truck driving platform with L4 (SAE) levels of safety.

Job Description

Our deep learning team helps autonomous trucks sense and perceive the world. You will play an important role in creating novel algorithms for advanced perception and applying your algorithm on terabytes of data. You will also work closely with other talents in this field in building the next-generation of autonomous sensing algorithms.

Responsibilities:

Research and prototype developing using deep learning with a special focus on the perception problems of autonomous driving

Qualifications:

MS/PhD in Computer Science/Electrical Engineering 3+ years of research or practical experience in applying deep learning on large scale and real world data Knowledge in deep learning topics including but not limited to detection, segmentation, 3D perception, and spatial-temporal analysis Strong coding skills in Python or C/C++ Familiar with at least one of the following deep learning frameworks: MXNET (preferred), TensorFlow, Pytorch, Caffe.

Preferred:

Track record of publishing in top-tier computer vision or machine learning conferences or/and journals. Prior academic or industrial experience in autonomous driving.

Perks

Competitive salary and benefits

Work with world class AI Engineers

Shape the landscape of autonomous driving

Daily breakfast, lunch, and dinner

Full kitchen with unlimited snacks and fruits

Medical, Vision, and Dental insurance plan

Company 401(K) program

Company paid life insurance

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.