Principal Machine Learning Researcher, On-Device Optimization
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The role
As a Principal ML Researcher focused on on-device optimization, you will bridge advanced research and product-ready deployment for edge AI systems.
You will lead research and implementation of model compression techniques such as quantization, pruning, distillation, and low-rank factorization.
You will develop methods to run state-of-the-art transformer and vision models on-device under hardware constraints.
You will drive hardware-aware training strategies to optimize latency, throughput, and memory usage, and collaborate with software engineers to integrate models into applications.
You will evaluate frameworks and quantization strategies (e.g., AWQ, GPTQ, SmoothQuant) and benchmark performance.
The role requires a PhD with related experience, expertise in edge ML, and strong collaboration and communication skills.
You will lead research and implementation of model compression techniques such as quantization, pruning, distillation, and low-rank factorization.
You will develop methods to run state-of-the-art transformer and vision models on-device under hardware constraints.
You will drive hardware-aware training strategies to optimize latency, throughput, and memory usage, and collaborate with software engineers to integrate models into applications.
You will evaluate frameworks and quantization strategies (e.g., AWQ, GPTQ, SmoothQuant) and benchmark performance.
The role requires a PhD with related experience, expertise in edge ML, and strong collaboration and communication skills.
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