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Software Engineer-Machine Learning Acceleration

Software Engineering, Advanced Technologies Group in Pittsburgh, PA

At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.

 

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.

We’re changing the way people think about transportation. Not that long ago we were just an app to request premium black cars in a few metropolitan areas. Now we’re a part of the logistical fabric of more than 600 cities around the world. Whether it’s a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.

 

For the people who drive with Uber, our app represents a flexible new way to earn money. For cities, we help strengthen local economies, improve access to transportation, and make streets safer.

 

And that’s just what we’re doing today. We’re thinking about the future, too. With teams working on new modalities, self-driving cars and even urban air transportation, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.

What You’ll Do

 

  • Optimize Machine Learning inference models and computer vision algorithms for lower latency, targeting the hardware architecture on which it executes

 

What You’ll Need

 

  • Education:  
    • BS / MS / PhD degree in Computer Science or related field
    • For non CS majors or BS candidates, strong industry software experience (2-5 years)
  • Experience:  
    • Strong programming skills in C++ or C
    • Deep Learning Frameworks: Caffe, TensorFlow, TensorRT, ONNX, PyTorch
    • Scripting/automation languages, including: Perl, Python and/or Bash  
    • Strong software development background demonstrated by industry experience in robotics, systems software, computer vision, or gaming
    • Strong analytical skills
    • Ability to learn new technologies quickly
    • Ability to work on large code bases
    • Comfortable working in a Linux development environment  

 

 

Bonus Points if

    • Knowledge of various low-level computer vision algorithms
    • Experience with heterogeneous compute platforms (CPU+GPU+FPGA)
    • Experience with Parallel Programming such as CUDA and/or OpenCL

 

 

About the Team

 

At the Advanced Technologies Group (ATG), we are building technologies that will transform the way the world moves. Our teams in Pittsburgh, San Francisco, and Toronto are dedicated to mapping, software and hardware development, vehicle safety, and operations for self-driving technology. Our teams are passionate about developing a self-driving system that will one day move people and things around more safely, efficiently, and cost effectively.

 

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

 

Our hardware team's role is to develop and integrate UBER self-driving technologies into existing and future vehicle platforms of multiple OEM’s at scale.

 


See our Candidate Privacy Statement

At Uber we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.