General Summary:
Qualcomm XR Research India is rapidly expanding to offer state of the art and futuristic XR solutions that would shape the future of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) use cases. At XR ML systems team, we are interested in optimally splitting the XR workloads across different compute devices to further nurture the Qualcomm devices ecosystem. We prefer candidates with a strong academic background in AI/ML/DL areas with a significant industrial experience in understanding and analyzing AI workflows, optimizing, integrating and packaging these algorithms on real-world AI products on embedded platforms.
Responsibilities:
As a dependable Individual Contributor in this role, you will be holding the following responsibilities.
Quickly comprehending SOTA AI algorithms implementing on Linux PC, migrating to Qualcomm HW, Profiling and Optimizing those algorithms
Systems thinking for dissecting any XR use-case to a set of AI/ML/DL algorithms and quickly building prototypes which are both accurate and efficient in Python and/or C++.
System level analysis of CV/AI/ML usecases and workloads and mapping algo blocks to HW accelerators like HTP, GPU and NSP
Quickly ramping up on Qualcomm ML tools like SNPE, QNN SDK and AIMET and using these tools to analyze and improve the performance of external and home-grown ML/DL algorithms on Qualcomm devices.
Strong Ownership on delivering high quality results and meeting project timelines by proactively unblocking constraints by interacting with key stakeholders in SW, Algorithms and Tooling teams.
Periodically communicating the progress to key-stake holders through oral and written methods, seek feedback and instrument solutions to address the gaps.
Minimum Qualifications:
4+ years of hands-on experience with developing, optimizing and deploying ML models on embedded platforms, with a Master’s degree in electrical engineering, information systems, computer science, or related field.
Very good proficiency in transformer-based DL algorithms
Very good proficiency in programming languages that include Python, C and C++.
Very good proficiency and hands on experience in State-of-the-art DL frameworks like PyTorch, ONNX and Tensorflow and opensource libraries.
Strong communication skills and inter-personal skills to work effectively in a team.
Preferred Qualifications:
Profiling of ML models on different target platforms, locating bottlenecks and suggesting workarounds
Hands-on experience with DNN optimization techniques like Quantization, Pruning and Network Arch Search
System level analysis of CV/AI/ML usecases and workloads, locating bottlenecks and optimizing for performance.
Optimal task-partitioning of CV/Ai/ML workloads across multiple compute devices for the best power-perf-accuracy tradeoffs
Systematically estimating power-perf-accuracy of AI algorithms on futuristic SOCs/ HW accelerators through compute, memory and bandwidth constraints is a huge plus
Hands on experience with Qualcomm tools like AIMET, QNN SDK and SNPE is a huge plus
Minimum Qualifications:
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