Machine Learning Intern

Job description

Machine Learning Intern

  

Job Description:

   

At HPE, we bring together the brightest minds to create breakthrough technology solutions and advance the way people live and work. Our legacy inspires us as we forge ahead dedicated to helping our customers make their mark on the world.

Learning does not only happen through training. Relationships are among the most powerful ways for people to learn and grow, and this is part of our HPE culture. In addition to working alongside talented colleagues, you will have many opportunities to learn through coaching and stretch assignment opportunities. You’ll be guided by feedback and support to accelerate your learning and maximize your knowledge. We also have a “reverse mentoring” program which allows us to share our knowledge and strengths across our multi-generation workforce.

Your responsibilities will include but are not limited to:

·  Support proactive hardware failure prediction using machine learning
·  Solid understanding of engineering fundamentals in field of study
·  Collaborate with experienced design, signal integrity, and Machine learning teams
·  Quickly come up to speed in complex environments

Required Skills and Background:

·  Master degree working towards completion of PhD program.
·  Machine learning experience
·  Strong problem solving skills
·  Strong verbal and written communication skills
·  Self-motivated and ambitious

We offer:

• A competitive salary and extensive social benefits

• Diverse and dynamic work environment

• Work-life balance and support for career development

• An amazing life inside the element! Want to know more about it?

Then let’s stay connected!

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Job:
Administration

Job Level:
N/A

    

Hewlett Packard Enterprise is EEO F/M/Protected Veteran/ Individual with Disabilities.

   

HPE will comply with all applicable laws related to the use of arrest and conviction records, including the San Francisco Fair Chance Ordinance and similar laws and will consider for employment qualified applicants with criminal histories.