Deep Learning Engineer, Camera, Imaging and Computer Vision

Key Skills

Java Programming, Python Programming, Ruby on Rails, Mechanical, Java Programming, Unix Operating system, logo, Offshore,

Job Description

The Camera, Imaging and Computer Vision team delivers Intelligent Vision & Imaging technologies and features underlying Rivian’s adventure user experiences. This multi-disciplinary team architects, designs, develops and productizes state-of-the-art algorithms and SW stacks on various compute platforms, and across multiple vehicle system architectures. As a Deep Learning Engineer, you will work on deep learning model training and evaluation for vehicle security, safety, and driver assistance. You will also expand your horizons to collaborate with other domain experts to bring our innovations from concept to product.

Responsibilities:

• Train, tune, and evaluate deep learning models in the areas of object detection, object tracking, depth estimation, segmentation, action recognition. 
• Research, design and drive efficient and effective model training and testing strategies for novel deep learning applications. 
• Collaborate with data scientists and test engineers to rigorously evaluate the performance of neural networks. 
• Assist computer vision engineers in deploying neural network models onto the vehicle 

Qualifications:

• MS or PhD in Computer Science, Software Engineering, Electrical Engineering, or a related field. 
• 5+ years of experience in deep learning in one of the following: object detection, segmentation, tracking, pose estimation, face recognition, action recognition.
• Solid Python skills and deep understanding of deep learning frameworks and workflow. 
• Experience in neural network model training/tuning/evaluation/optimization. 
• Proficiency in TensorFlow and PyTorch. 
• Hands-on experience working in computer vison using models and methods such as CNN, Transformer, GAN, etc. 
• Good written communication and presentation skills. 

Preferred Qualifications:

• Experience in transfer learning, multitask learning, domain adaptation. 
• Experience in automotive electronics and/or consumer electronics. 
• Strong ability in problem solving and driving for results. 
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as CVPR, ECCV/ICCV, SIGGRAPH, ICLR, or similar.