Machine Learning — PhD Intern - Calabasas, CA
1 month ago
Keysight is at the forefront of technology innovation delivering breakthroughs and trusted insights in electronic design simulation prototyping test manufacturing and optimization Our ~15 employees create world-class solutions in communications 5G automotive energy quantum aerospace defense and semiconductor markets for customers in over countries Learn more about what we do Overview Keysight s Applied AI Research group is developing adaptive neural modeling frameworks that enable AI systems to evolve across changing simulation measurement experimental conditions This internship focuses on building dynamic I/O schema mechanisms for neural networks implemented in libtorch C++ API for PyTorch Your work will enable AI models to flexibly adapt to new use cases reduce retraining time lower compute costs while improving scalability reusability across Keysight s engineering AI ecosystem Role Overview As a PhD Intern in Machine Learning Systems you will design prototype runtime-adaptive neural models capable of modifying their input/output schemas dynamically You ll collaborate with machine learning runtime simulation experts to integrate these mechanisms into Keysight s AI modeling stack driving more efficient versatile future-proof model workflows What This Internship Offers A research opportunity to develop next-generation adaptive neural architectures in C++/libtorch Mentorship from experts in AI runtime systems model architecture design computational modeling Direct impact on improving AI model adaptability reducing retraining costs enabling modular reusability Responsibilities Design implement dynamic I/O schema functionality support runtime addition removal inputs outputs Develop modular components maintain consistent state gradient flow evolving input/output configurations Create schema translation mapping utilities maintain backward compatibility incremental fine-tuning Integrate schema management training checkpointing inference workflows Benchmark adaptability retraining efficiency quantifying improvements compute utilization convergence time Collaborate with runtime engineers ensure performance memory stability model safety under dynamic schema changes Document publish experimental results architecture designs internal research dissemination Qualifications Current PhD student recently graduated PhD Machine Learning Computer Science Applied Mathematics Electrical Mechanical Engineering Strong proficiency C/C++ libtorch C++ PyTorch API neural network implementation Understanding dynamic computation graphs model serialization runtime configuration management Experience designing training modular neural architectures or run-time adaptive ML systems Familiarity schematic evolution metadata management flexible I/O processing Strong analytical software engineering skills attention efficiency safety reusability Prerequisites Experience modern CMake workflows multi-threading async I/O memory management high-performance ML applications Competence version control profilers testing frameworks Commitment creating robust extensible systems making neutral modeling adaptable efficient
Job description
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