Autonomy - Contributor DSP and Machine Learning Real Time Optimization

Qualcomm

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Job Detail
Job Id E1958961
Job Title Autonomy - Contributor DSP and Machine Learning Real Time Optimization
Post Date 09/20/2017

Company

-

Division

Qualcomm Technologies, Inc.

-

CDMA Technology


Job Area Engineering - Systems
Location India - Bangalore
Job Overview

Qualcomm Autonomous Driver Assistance Systems (ADAS) Qualcomm is utilizing its traditional strengths in digital wireless technologies to play a central role in the evolution of automotive infotainment and autonomous driving. We are investing in several supporting technologies including 4G, 5G, ADAS, and Deep Learning. The Qualcomm ADAS Systems team will optimize DSP, computer vision and machine learning algorithms for the Qualcomm ADAS Systems architecture. We welcome smart, energetic systems engineers with a passion for ADAS algorithms (both traditional computer vision and machine learning), real-time systems design, implementation, and optimization to join our growing, multisite development team. This position is for a hands-on engineering role in implementing computer vision, multi-sensor fusion, and machine learning algorithms for ADAS as well as optimizing them exercising the full capability of the Qualcomm Snapdragon platform. Work assignments require theoretical and practical knowledge of computer vision, machine learning, DSP programming (including deep optimizations for memory and cycles), and embedded systems architecture. The candidate should also be strong in C / C++ programming and programming of hardware accelerators for computer vision and/or machine learning.

Minimum Qualifications 5+ years of experience in the following:

Developing algorithms on a DSP, GPU or any vision accelerator like CEVA
Developing high-performance low-bandwidth algorithms on programmable hardware architectures.
Software systems design for multi-threaded processors and heterogeneous processor SoCs including IPC, shared memory
Preferred Qualifications Experience developing and optimizing algorithms on fixed point processors with limited power and memory. Experience in developing and optimizing Vision or image based algorithms (Image Stitching, Lane Detection, Pedestrian Detection using HOG/SVM etc.) Experience developing vision libraries on embedded processors for applications involving Image processing, Feature Extraction, Object Detection, 3D Reconstruction, and Clustering Excellent knowledge of compiler-based optimization tricks for DSPs, VLIW and SIMD processors. Experience working with real time processing of camera and sensor data Familiarity with DL frameworks such as Caffe, Tensorflow and Theano. Experience developing or porting Neural Network layers into an embedded processor or a GPU using OpenCL is a plus Candidates should be able to optimize NN layers & Signal processing algorithms for both bandwidth and performance Experience developing math libraries like GEMM/BLAS on an embedded processor or a SIMD processor would be a plus Experience in Inference optimization on a SIMD embedded processor is a plus Familiarity with state of the art DL approaches for Vehicle/Pedestrian detection using SSD/RCNN/YOLO etc Excellent knowledge of computer architecture including SIMD, VLIW and RISC processors. Ability to understand and articulate the impact of specific algorithms on System wide parameters like DDR traffic, System bandwidth, Processing latency. Ability to analyze existing algorithms, clearly identify bottle-necks and provide recommendations for next-generation HW. Experience in performance analysis on heterogeneous compute platforms such as Snapdragon SOCs is a plus. Experience in working with large software systems designed to layered, modular software architecture. Experience in working with profiling tools and software debuggers.
Education Requirements Required: Bachelor's, Computer Science and/or Electrical Engineering
Preferred: Master's, Computer Science and/or Electrical Engineering
Keywords Automotive Driver Assistance Systems, Computer Vision, Machine Learning, DSP optimization, embedded systems, sensor fusion
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Job Id E1958961
Job Title Autonomy - Contributor DSP and Machine Learning Real Time Optimization
Post Date 09/20/2017

Company

-

Division

Qualcomm Technologies, Inc.

-

CDMA Technology


Job Area Engineering - Systems
Location India - Bangalore
Job Overview

Qualcomm Autonomous Driver Assistance Systems (ADAS) Qualcomm is utilizing its traditional strengths in digital wireless technologies to play a central role in the evolution of automotive infotainment and autonomous driving. We are investing in several supporting technologies including 4G, 5G, ADAS, and Deep Learning. The Qualcomm ADAS Systems team will optimize DSP, computer vision and machine learning algorithms for the Qualcomm ADAS Systems architecture. We welcome smart, energetic systems engineers with a passion for ADAS algorithms (both traditional computer vision and machine learning), real-time systems design, implementation, and optimization to join our growing, multisite development team. This position is for a hands-on engineering role in implementing computer vision, multi-sensor fusion, and machine learning algorithms for ADAS as well as optimizing them exercising the full capability of the Qualcomm Snapdragon platform. Work assignments require theoretical and practical knowledge of computer vision, machine learning, DSP programming (including deep optimizations for memory and cycles), and embedded systems architecture. The candidate should also be strong in C / C++ programming and programming of hardware accelerators for computer vision and/or machine learning.

Minimum Qualifications 5+ years of experience in the following:

Developing algorithms on a DSP, GPU or any vision accelerator like CEVA
Developing high-performance low-bandwidth algorithms on programmable hardware architectures.
Software systems design for multi-threaded processors and heterogeneous processor SoCs including IPC, shared memory
Preferred Qualifications Experience developing and optimizing algorithms on fixed point processors with limited power and memory. Experience in developing and optimizing Vision or image based algorithms (Image Stitching, Lane Detection, Pedestrian Detection using HOG/SVM etc.) Experience developing vision libraries on embedded processors for applications involving Image processing, Feature Extraction, Object Detection, 3D Reconstruction, and Clustering Excellent knowledge of compiler-based optimization tricks for DSPs, VLIW and SIMD processors. Experience working with real time processing of camera and sensor data Familiarity with DL frameworks such as Caffe, Tensorflow and Theano. Experience developing or porting Neural Network layers into an embedded processor or a GPU using OpenCL is a plus Candidates should be able to optimize NN layers & Signal processing algorithms for both bandwidth and performance Experience developing math libraries like GEMM/BLAS on an embedded processor or a SIMD processor would be a plus Experience in Inference optimization on a SIMD embedded processor is a plus Familiarity with state of the art DL approaches for Vehicle/Pedestrian detection using SSD/RCNN/YOLO etc Excellent knowledge of computer architecture including SIMD, VLIW and RISC processors. Ability to understand and articulate the impact of specific algorithms on System wide parameters like DDR traffic, System bandwidth, Processing latency. Ability to analyze existing algorithms, clearly identify bottle-necks and provide recommendations for next-generation HW. Experience in performance analysis on heterogeneous compute platforms such as Snapdragon SOCs is a plus. Experience in working with large software systems designed to layered, modular software architecture. Experience in working with profiling tools and software debuggers.
Education Requirements Required: Bachelor's, Computer Science and/or Electrical Engineering
Preferred: Master's, Computer Science and/or Electrical Engineering
Keywords Automotive Driver Assistance Systems, Computer Vision, Machine Learning, DSP optimization, embedded systems, sensor fusion
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