Sr Engineer, Machine Learning Engineering (Heterogenous SW, Adreno GPU)
Qualcomm
·Nov 12, 2025
- Location
- San Diego, CA,US, US
- Type
- Full-time
- Department
- Engineering
- Education
- PhD
- Source
- Eightfold
Description
Strong theoretical background in AI and general ML techniques Proven hands-on experience evaluating and optimizing Generative AI workflows for accuracy, performance, and other key metrics ML Accelerator architecture knowledge & experience with ML Accelerators - GPU/NPU Proven hands-on experience building LLM stacks to solve real-world problems Strong Python and modern C++ design and implementation skills. Strong command of Machine Learning training frameworks (i.e. PyTorch ) Proven hands-on experience establishing a high-quality software delivery process using industry best-practices (code review, CI/CD, automation, etc.) Strong Linux command line skills. Strong general analytical and debugging skills. Prior experience working in agile environments. Prior experience in collaborating with multi-disciplinary teams across time zones. Strong team player, communicator, presenter, mentor, and teacher. Hands-on experience developing software for heterogenous platforms with OpenCL. Experience developing solutions that target a variety of hardware ( CPU, GPU, NSP ) along with their respective SW development environments. Prior experience with model quantization, profiling and running models on edge devices. Experience developing SDKs/libraries targeting multiple platforms and operating systems (Android, Linux, Windows) Previous experience with Docker and Git. Master's and/or PhD degree in Computer Science, Engineering, Information Systems, or related field and 5+ years of work experience in Software Engineering, Systems Engineering, or related. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field.
