ML Software Engineer

KLA Tencor   •  

Ann Arbor, MI

Industry: Manufacturing

  •  

Less than 5 years

Posted 31 days ago

Company Overview

Calling the adventurers ready to join a company that's pushing the limits of nanotechnology to keep the digital revolution rolling. At KLA, we're making technology advancements that are bigger—and tinier—than the world has ever seen.

Who are we? We research, develop, and manufacture the world's most advanced inspection and measurement equipment for the semiconductor and nanoelectronics industries. We enable the digital age by pushing the boundaries of technology, creating tools capable of finding defects smaller than a wavelength of visible light. We create smarter processes so that technology leaders can manufacture high-performance chips—the kind in that phone in your pocket, the tablet on your desk and nearly every electronic device you own—faster and better. We're passionate about creating solutions that drive progress and help people do what wouldn't be possible without us. The future is calling. Will you answer?

Group/Division

KLA has always had a close relationship with data. Our optical and electron beam inspection and measurement tools use sophisticated data analytic techniques to provide value for our customers.


In this role, you will be part of a world class team of data science researchers, machine learning and application engineers who build and use a world class platform for development of Industrial AI application.


We are looking for engineers with a passion for data science and software engineering who can rapidly incorporate evolving ideas from academia and industry into useful solutions for our internal and external customers.

Responsibilities

  1. Build composite model independent workflows for standard ML operations such as feature selection, hyper parameter tuning, comparison to production baseline etc.
  2. Build solutions for decreasing wallclock time of training. Potential solutions would include caching of training data, distributed training etc.
  3. Build standardized solution for documenting modeling work. Support tools necessary for reviewing and approving such work.
  4. Build mechanisms for versioning models and storing metadata information about them.
  5. Define and own all metrics (coverage, scale, latency, reliability etc) for modeling area.

Qualifications

Doctorate (Academic)ORMaster's Level Degree with at least 2 years of experience.ORBachelor's Level Degree with at least 3 years of experience.

Minimum Qualifications

Doctorate (Academic)ORMaster's Level Degree with at least 2 years of experience.ORBachelor's Level Degree with at least 3 years of experience.

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