Responsibilities
Drive feature adoption and maximize performance on Windows AI platforms by developing system-level solutions and resolving complex integration and performance issues.
Profiling and optimizing system performance, applying knowledge of advanced DSP architecture and microarchitecture-level optimizations to achieve low latency and low power.
Collaborating with cross-functional teams (hardware, tools, and firmware) to evaluate design trade-offs and incorporate and drive adoption of features that improve performance.
Collaborate with cross-functional and global teams to develop best-in-class, high-performance AI solutions that can be leveraged by third-party Windows application developers.
Perform competitive benchmarking of AI tools, workflows, and state-of-the-art models across industry-leading platforms.
Drive continuous improvements in AI model performance, efficiency, and accuracy on Windows on Snapdragon platforms.
Skills and Experience
Bachelor’s or Master’s degree in Computer engineering, Electrical Engineering, Computer Science, or a related field.
3-7 years of experience in AI/ML application development. (multiple levels of openings)
Strong programming skills in C, C++, and Python.
Experience architecting, designing, and implementing software frameworks for complex heterogeneous or multi-processor systems.
Good understanding of the end-to-end AI software stack, including model development, optimization, deployment, and hardware acceleration technologies.
Hands-on experience optimizing AI models for performance, power, and accuracy on hardware accelerators such as CPUs, GPUs, and NPUs.
Strong understanding of modern machine learning architectures, including CNNs, RNNs, Transformers, and ensemble methods.
Proficiency with AI frameworks and tooling such as PyTorch, TensorFlow, ONNX Runtime, and MLIR.
Experience using processor-specific optimization tools, libraries, and profiling and optimization tools to improve AI performance on CPUs, GPUs, and NPUs is a plus.
Excellent written and verbal communication skills.
Demonstrated ability to learn quickly, think critically, and adapt in a fast-paced and rapidly evolving technology environment.
Good to have
Prior experience with Qualcomm FastRPC and Windows MCDM drivers would be good to have
Experience with transformer-based models such as BERT, GPT, T5, and Llama. Experience developing, optimizing, or deploying large language models (LLMs) or foundation models is a strong plus.
Practical experience with developing middleware or system software preferably for AI applications
Minimum Qualifications
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field.
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