Job description


  • Entry level
  • No Education
  • Salary to negotiate


About Uber
At ATG, we are ambitious, engaged and excited about transforming the way the world moves. With locations in Pittsburgh, San Francisco, and Toronto, the Advanced Technologies Group is comprised of Uber’s self-driving engineering team dedicated to self-driving technologies, mapping, and vehicle safety. Our teams are passionate about developing long-term technologies that advance Uber’s mission of bringing safe, reliable transportation to everyone, everywhere.
At Uber, we believe technology has the power to make transportation more efficient, accessible, and safer than ever before. Self-driving technology has the potential to make these benefits an everyday reality for our customers, but it’s not going to happen overnight.  Building best-in-class self-driving technology will take time, and safety is our priority every step of the way. Operating inclusively and transparently, while displaying responsible behavior in a structured development are critical to safety. We at ATG seek candidates who will role model these values.
Research interns at Uber ATG R&D will have the flexibility to pursue a range of different directions in research and application. Some projects might involve fundamental machine learning and AI research, pushing the frontiers of the field by developing new algorithms for learning and control. Others could include devising and training new models based on Uber’s huge self-driving car data sets. These models may be seen through to production where they will impact millions. Interns will have the opportunity to work directly with a distinguished group of researchers and engineers in ATG Research group, and across other product and operations teams at Uber ATG.
An internship will last for 12 weeks or more and will span a time-frame that is suitable for the candidate. During this period, Interns will meet with researchers and teams across Uber ATG and converge on initial project directions, and mentorship will be arranged.Interns will be encouraged to publish their work externally at top machine learning venues (NIPS, ICLR, ICML, CVPR, EMNLP, ACL, etc.), via blog posts, or by posting open source projects.
Eligibility Requirements
Actively pursuing BS, MS, or PhD in a technical field, and returning back to school after the internship is complete.
Coursework in or working knowledge of linear algebra, calculus, and probability.
Coding ability in Python or C/C++
Demonstrated experience with machine learning or deep learning algorithms, and demonstrated passion for the same. If you’re excited about ML, show us what you’ve done so far!
Preferred Qualifications
Demonstrated success at self-driven projects: whether you coded something great from the ground up, executed a beautiful research project, organized a group of your peers to action, asked and answered an important societal question, or started a company, we’re looking for people that are used to pressing toward ambitions, exciting goals.
A strong track record of open source contributions in any area, particularly machine learning. Impactful, well-cited publications in any field, particularly machine learning. Interns will work from Uber ATG’s R&D locations (San Francisco, CA OR Toronto, Canada). We strongly encourage applications from candidates with diverse backgrounds and from anywhere in the world! Candidates who need a visa to work in the US are also encouraged to apply; we will investigate options on a case-by-case basis for those who are accepted.

About the company

Uber Technologies Inc. is an American international transportation network company headquartered in San Francisco, California. The company develops, markets and operates the Uber mobile app, which allows consumers with smartphones to submit a trip request which is then routed to Uber drivers who use their own cars. By May 28, 2015, the service was available in 58 countries and 300 cities worldwide. Since Uber's launch, several other companies have copied its business model, a trend that has come to be referred to as "Uberification".

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