Black Sesame Technologies is a VC-funded unicorn start-up founded in 2016. We specialize in artificial intelligence and algorithms for autonomous driving, smartphones, and other consumer electronics.
Our team works on exciting technologies changing the AI landscape, and we are looking for the right Engineer with extensive experience in image processing, computer vision, and deep learning to join our team.
At Black Sesame Technologies, you will get the chance to work together with an amazing development team. This role is highly multi-functional, and you will work closely with the various highly skilled software development/ ML teams developing groundbreaking algorithms.
- Develop image/video processing algorithms for camera-related applications, such as mobile, surveillance, and vehicle.
- Collaborate with software and hardware teams to optimize algorithms for on-device real-time implementation.
- Explore the latest technologies and propose innovative ideas.
- MS/Ph.D. in computer science or electrical engineering; alternatively, a comparable industry career, with significant experience in delivering products using state-of-the-art computer vision and machine learning technologies.
- Familiar with deep learning frameworks, such as such as PyTorch and/or TensorFlow.
- Familiar with at least one of stereo depth, mono depth, and point cloud processing.
- Experience in developing deep learning models with 2D/3D tracking & ReID, 2D/3D object detection, depth estimation from single/multiple cameras etc.
- Understanding of computer vision concepts, such as optical flow, 3D visions, camera coordination, and sensor/camera calibration.
- Experience in surveillance or V2X roadside sensor 2D/3D detection and localization with one or more sensors of camera, radar and lidar is a plus.
- Demonstrate great teamwork, outstanding communication skills, and willingness to learn.
- Passion on cutting-edge computer vision and machine learning technologies.
- Solid programming skills with C/C++ in a rapid prototyping environment.
- Experience in CNN model training on a large-scale dataset.