Engineer- Python Development – QA-ML

Full Time
  • Full Time
  • India
  • Applications have closed

As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud-edge software, applications, and/or specialized utility programs that launch cutting-edge, world-class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.

Key Responsibilities:
– Expertise in developing test cases, automating tests, executing test cases, and troubleshooting/analyzing issues
– Experience in Electron-based application testing across multiple operating systems (Windows, Linux, macOS)
– Experience with testing frameworks such as Jasmine and Playwright (for Electron JS-based desktop applications)
– Strong development skills in Python
– Strong understanding of machine learning and deep learning workflows
– Experience with at least one machine learning framework such as TensorFlow or PyTorch
– Excellent communication skills (verbal, presentation, and written)
– Strong problem-solving, time management, analytical, and debugging skills
– Ability to work effectively as a team member and operate in a self-driven manner

Qualification Required:
– Bachelor’s degree in Engineering, Information Systems, Computer Science, or related field
– Masters or Bachelors degree in Computer Science, Computer Engineering, or Electrical Engineering

Join a new and growing team at Qualcomm focused on advancing the state of the art in machine learning. The team uses Qualcomm chips extensive heterogeneous computing capabilities and engineers them to enable the execution of trained neural networks on device without the need for a cloud connection. You will work with the latest DNNs emerging from the research community and keep pace with fast-moving developments in industry and academia to continuously enhance benchmarking and validation infrastructure from both software engineering and machine learning perspectives. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud-edge software, applications, and/or specialized utility programs that launch cutting-edge, world-class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.

Key Responsibilities:
– Expertise in developing test cases, automating tests, executing test cases, and troubleshooting/analyzing issues
– Experience in Electron-based application testing across multiple operating systems (Windows, Linux, macOS)
– Experience with testing frameworks such as Jasmine and Playwright (for Electron JS-based desktop applications)
– Strong development skills in Python
– Strong understanding of machine learning and deep learning workflows
– Experience with at least one machine learning framework such as TensorFlow or PyTorch
– Excellent communication skills (verbal, presentation, and written)
– Strong problem-solving, time management, analytical, and debugging skills
– Ability to work effectively as a team member and operate in a self-driven manner

Qualification Required:
– Bachelor’s degree in Engineering, Information Systems, Computer Science, or related field
– Masters or Bachelors degree in Computer Science, Computer Engineering, or Electrical Engineering

Join a new and growing team at Qualcomm focused on advancing the state of the art in machine learning. The team uses Qualcomm chips extensive heterogeneous computing capabilities and engineers them to enable the execution of trained neural networks on device without the need for a cloud connection. You will work with the latest DNNs emerging from the research community and keep pace with fast-moving developments in industry and academia to continuously enhance benchmarking and validation infrastructure from both software engineering and machine learning perspectives.

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