Machine Learning, Recommender Systems Intern

Company: Signify North America Corporation
Location: Cambridge
Posted on: November 21, 2020

Job Description:

We are Signify, the new company name of Philips Lighting.

We re the world leader in lighting for professionals, consumers and for the Internet of Things. Our passion for sustainability makes us one of the top 10 greenest companies in the world.

See #SignifyLife through the eyes of our employees!

Working as a MACHINE LEARNING, RECOMMENDER SYSTEMS INTERN at Signify is dynamic. You will work with top technology partners, learn new skills, and grow your career. Your work will make a huge and high-tech impact with the world of lighting. We have over 16,000 patents and invest around 5% of our sales in R&D. We are looking for technical professionals who will take our innovations and help us invent the future of lighting.

This internship is a part of Signify s Research team located in CAMBRIDGE, MA and is considered a center of excellence on Artificial Intelligence and Machine learning Innovation for Signify. The team works together with the Signify global Research, businesses, and functions on AI/ML. Its responsibilities are at the heart of driving AI/M innovations for systems, new IoT propositions, and innovative apps. You will learn and apply new cutting-edge techniques that will be rewarding for your career. With a great freedom to innovate, you will play an active role and grow yourself on AI and ML.

As a MACHINE LEARNING, RECOMMENDER SYSTEMS INTERN, you will work from JUNE 7TH TO AUGUST 20TH. These are not fixed dates and can be adjusted. This internship could also lead to a full time at-will position.

WHAT YOU LL DO

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Design context-aware adaptive scene recommendation solutions for our connected home lighting systems.
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Build an AI engine that becomes core part of the proposed solution.
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Demonstrate, publish (papers) and protect (patents) novel.

WHAT YOU LL NEED

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Must be able to work in the United States without corporate sponsorship now and within the future.
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A graduate-level student (PhD preferred) in computer science, or any related disciplines.
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Experience in analyzing variety of data at scale.
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Excellent understanding of machine learning concepts and algorithms.
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Expertise in developing recommender systems.
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Experience with big data analytics pipeline in AWS or any cloud provider.
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Good publication record, including publications in machine learning venues.
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Programming experience in Python or a similar language is required. Knowledge of PySpark is a big plus.

WHAT YOU LL GET IN RETURN

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Hands-on experience working with a Research team that you will partner with to solve complex problems for millions of Hue Smart Lighting users.
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Contribute to Intellectual Property (Patents and Trade-secrets) and publish in top peer-reviewed conferences and journals.
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Opportunity to learn and apply techniques to real world problems.

WHAT WE PROMISE

We re committed to the continuous development of our employees, using our learning to shape the future of light and create a sustainable future. Join the undisputed leader in the lighting industry and be part of our diverse global team. #LI-FM1 #WeAreSignify #SignifyLife.

Concerning US based roles: Signify is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex (including pregnancy), sexual orientation, gender identity, national origin, genetic information, creed, citizenship, disability, protected veteran or marital status. As an equal opportunity employer, Signify is committed to a diverse workforce. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veterans Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants that require accommodation in the job application process may contact 888-367-7223, option 5, for assistance.

Keywords: Signify North America Corporation, Cambridge , Machine Learning, Recommender Systems Intern, Other , Cambridge, Massachusetts