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.
Research and prototype development using deep learning algorithms with a special focus on the perception modules including Camera, LiDAR, Radar, and Sensor Fusion.
Deliver high-quality and reliable code and integrate with the perception system on the L4 autonomous driving trucks.
Master in Computer Science, Electrical Engineering, or other related field.
3+ years of research or practical experience in applying deep learning algorithms on large scale and real world data.
Strong knowledge in deep learning related topics including but not limited to detection, segmentation, 3D perception (image, point cloud), and spatial-temporal analysis.
Strong coding skills in Python and C++.
Familiar with Linux and deep learning frameworks such as MXNET (preferred)/PyTorch/TensorFlow.
Ph.D. in Computer Science, Electrical Engineering, or other related field.
Prior academic or industrial experience for solving perception problems in autonomous driving.
Track record of publishing in top-tier computer vision or machine learning conferences or/and journals.
Visa sponsorship is available for this position
Opportunity for professional growth and career advancement
Competitive salary and benefits
Daily breakfast, lunch, and dinner
Shape the landscape of autonomous driving
100% Company paid Medical, Vision, and Dental insurance plan
Company 401(K) program
Company paid life insurance
Company paid education/training.
Company paid gym membership.
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.