Senior/Lead Senior Engineer, XR ML systems

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
• Masters 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:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience
• ORMaster's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience
• ORPhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience