👨🏻‍💻 Senior Software Engineer - On-Device ML

As an On-Device Machine Learning (ML) Frameworks Engineer, you will play a crucial role in developing and optimising machine learning models to run efficiently on various devices such as smartphones, tablets, AI PC and embedded systems. Your work will involve collaborating with cross-functional teams to integrate ML models into applications, ensuring they are both effective and resource-efficient. This role is vital in pushing the boundaries of what's possible with on-device AI, contributing to the enhancement of user experiences and the innovation of new features. Responsibilties Optimize machine learning models, ML Graph Conversion Stack & ML Inference Stack for deployment on edge devices. Develop and maintain OnDevice ML Inference Framework for specialized and general purpose processors Collaborate with software engineers, data scientists, and product managers to integrate ML solutions into products. Implement techniques to ensure efficient inference and minimal resource consumption on target devices. Conduct performance evaluations and continuous improvement of ML models & Inference pipeline post-deployment. Stay updated with the latest advancements in on-device ML technologies and frameworks. Troubleshoot and resolve issues related to ML model deployment and execution on devices. Qualifications Strong programming skills in languages such as C++, C and Python Must have experience with in-depth working and core implementation of machine learning frameworks such as TensorFlow Lite, PyTorch Mobile, ONNX or Core ML Strong experience and proven track record with intrinsic level (SIMD, NEON, AVX) implementation for optimizing compute and memory algorithms Proven track record of deploying ML models on edge devices and optimizing them for performance and memory Familiarity with performance profiling tools and techniques for mobile and embedded platforms. Solid understanding of computer architecture and hardware acceleration techniques. Effective communication skills and the ability to work collaboratively in a team environment. Bachelor’s or Master’s degree in Computer Science, Electronics Engineering, or a related field