Website
KLA

Responsibilities

We’re transforming the digital age by enabling the manufacture of ICs at next generation technology nodes – 5 & 7 nm design rules. This is done by pushing the boundaries of optics, sensors, image processing, machine learning and computing technologies, creating systems capable of finding defects as small as 10 nm at data rates of 50 GB/second. If you are passionate about driving R&D in advanced deep learning, 3D sensor fusion, Bayesian & Physics based Machine Learning, advanced Neural & HPC architectures, then come join over 500 PhD researchers working at KLA. We actively collaborate with research institutes all over the world with our R&D centers in the San Francisco Bay Area, Shanghai, Tel-Aviv and Chennai. Our corporate office is located in Silicon Valley, Milpitas, California.

Categories of R&D Openings

Deep Learning Researcher: These positions will focus on research areas to drive investigations in open areas such as architectures for Unsupervised Outlier Detection, Semi-supervised deep learning for process control applications with stationary and non-stationary signals, as well as feature extraction discovery. The ideal candidate should preferably have a Ph.D. in Bayesian Machine Learning or related areas with extensive software skills to build and research machine learning systems.

Physics Based Machine Learning Researcher: These positions will focus on exploring research in building architectures to exploit explicit knowledge of physics, sensor fusion and associated prior knowledge with Bayesian based generative models to solve problems in building predictive detection of systematic and quasi-random events in semi-conductor manufacturing. Ideal candidates should have a computational physics, chemistry or biology background combined with deep interest and knowledge of Bayesian approaches to machine learning with extensive software skills to build and research machine learning systems.

Image Processing & Machine Learning Researcher: These positions will focus on exploring research in the areas of image processing and computer vision with the aim of building automated machine vision systems in a range of application areas from semi-conductors to satellite scene analysis to driverless cars. The ideal candidate should have a PhD in the related areas of Image Processing, Computer Vision with an emphasis on Bayesian based machine learning with extensive software skills to build and research machine learning systems.

Deep Learning HPC Architectures & Data Systems Engineers: These positions will explore research and development for novel HPC architectures to enable ultra-high performance training and inference on very high bandwidth streaming video data. The ideal candidate should have an MS or PhD in HPC architectures, Hardware Data Flow architectures and practical proven experience in building both scale-in and scale-out systems with familiarity of both SIMD as wells GP-GPU architectures.  The person should also have a keen interest in the deep learning application space with the ability to map complex deep neural architectures to practical system implementations.

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