Machine Learning Intern

| Greater Boulder Area
Please send your resume to [email protected]
Apply now

TruU is solving the problem of frictionless user identity - giving users trusted access to digital and physical sites without passwords. 
 
We are a startup environment that is focused on empowering the team to architect and implement elegant solutions to the interesting and challenging problems before us, in an enjoyable environment that fosters our own professional and personal growth.  While we don't have farm-to-table catered lunches or a climbing wall in our offices, we work hard and deliver consistently, but are not under pressure to maximize in-seat hours.  This means that TruU team members have both ownership and responsibility as we pioneer frictionless converged identity.  We have offices in Downtown Boulder, Denver, and Palo Alto. 

If you think you might be a good match for an internship at TruU, please send a resume along with a note on why you think we should hire you. Please email us at [email protected]

Location: Downtown Boulder (Pearl Street) 
Duration: Fall 2019

If you are a strong computer science person with who wants to get into machine learning, this internship can help you get started into the field. We expect you to be strong analytically, with a strong attention to detail. You will help us create and clean machine learning data sets, train and test machine learning models, and write python programs to automate these tasks. This paid internship is expected to take 20 hours per week and expected to last the fall semester. 

There is a possibility of an extension for Spring.  

Skills and Experience 

  •   A senior in computer science, or a related field  
  •   Curiosity about data with a data driven decision making attitude
  •   Experience with python, or another scripting language
  •   Strong attention to detail
  •   Team player with demonstrated communication skills
     
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Please send your resume to [email protected]
Apply now
Please send your resume to [email protected]
Apply now
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