AI ML ADAS- Senior
AI ML ADAS- Senior
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Job Description
About SRM Tech
SRM Tech is a global IT services company specializing in automotive technologies digital transformation product engineering and supply chain optimization. We empower businesses across North America Japan Europe and India with cuttingedge solutions in technology consulting platform development data analytics AI/ML cloud and infrastructure embedded software and endtoend product design to manufacturing.
As a Great Place to Workcertified company we are committed to fostering an environment where our employees can learn grow and thrive. We believe that when our people are empowered and valued they drive innovation take care of our customers and contribute to our shared success.
At SRM Tech every contribution matters and we are always looking for the right talent to join us on this journey!
ADAS AIML Computer vision development and Radar Algo development
Experience in the areas of Computer Vision & Machine Learning
1. Deep knowledge & experience of :
2. Image processing Computer Vision Machine / Deep LearningCamerasMono camera stereo camera OR LIDAR
3. Stereo Camera & Lidar 3D point cloud processing 2D and 3D coordinate rotation and translation
4. Mono & Stereo Camera Calibration & Imaging Static Online Dynamic Camera calibrations
5. 3D reconstruction & depth imaging
6. Simultaneous Localization & Mapping SLAMopt
7.Machine learning techniques and algorithms
8.Building productiongrade Deep learning and Neural Network models using Computer vision techniques
9.Experience with imaging or optics systems.
RADAR
10. Strong understanding of concepts and background in development of topics like Kalman or extended Kalman filters particle filter clustering techniques etc.
11. performance measurement for algorithms like sensor fusion object tracking etc.
12. Expertise in Radar and deep learning or machine learning techniques on embedded platform
Requirements
Proficiency in:
1.Embedded C C / Python/ Matlab
2.Image Processing and computer vision libraries like OpenCV and MATLAB image processing and computer vision toolbox
3.Programming with strong experience in Data structures algorithms and software development
4. Either of the Deep Learning frameworks such as TensorFlow PyTorch Caffe MxNet Any 1
5. Common Data science tool kits
6. Embedded OS Linux Open GL Open CL
7. Deploying Computer vision and machine learning algorithms on edge computing hardware
8. Sensor fusion of Camera LIDAR RADAR GNSS etc and perception system
9. Strong mathematical background
10. Ability to perform complex engineering work with minimal guidance
Embedded Computer vision ADAS and Computer vision algorithm porting experience with software development using C/C++ on embedded platforms (e.g. ARM, DSP and GPU) 1.Hands on experience on hardware platforms - TI (Texas Instruments TDA4, TDA3x, TDA2X)/ Renesas/ NXP/ Intel/Qualcomm equivalent 2. OPenVX, OpenCL, OPen GL programming in ARM, DSP, GPU for Computer vision algorithms, Rendering 3.Advance knowledge of C/C++ in Embedded LINUX, OpenCV, SYSBIOS, FreeRTOS, Experience TDA4xx SoC family and In-depth understanding of video concepts. e.g. video encoding, image processing, transforms, color spaces etc. 4. Should have a good, hands-on experience in developing and optimising DSP and Neural Networks 5. Applications in C and assembly language of a DSP, Vector processing and ARM SIMD NEON. strong grasp of the issues software performance tuning. 6.Work experience in DSP firmware and optimized neural network kernel implementations 7, Operating System - Linux/ QNX/ SYSBIOS/ Vxworks/ Integrity/ ROS, any other POSIX RTOS 8. Porting Algorithm experience in robotics, ultrasonics, RADAR, (or) LIDAR, sensor fusion, computer vision in Embedded Platforms. 9.Developing and Optimising performance of Signal Processing firmware and neural network kernels using C level changes using C intrinsic. 10.Hands on knowledgeexperience on camera/computer vision pipeline, development, porting and optimization
Education
Diploma / B.E. / B.Tech / M.Tech / M.E. (ECE,EEE)
Employment Type
Full Time
