Research Engineer, Self Driving

Job description

About Uber

 

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 autonomous trucking and self-driving cars, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.

About the Role

 

You will participate in the unique effort of bringing innovative state-of-the-art deep-learning models for self-driving into production, and onto autonomous vehicles. You will collaborate closely with a team of highly skilled researchers and engineers, tackling an array of challenges related to applying machine learning to self-driving vehicles. You will work on a variety of research engineering tasks related to algorithms for detection & perception, prediction, motion planning & automated map production, to name a few.

What You’ll Do

 

·  Develop in-depth understanding of deep learning models and algorithms and contribute to optimizing their training protocols, as well as test-time performance, runtime, memory footprint, and power consumption.
·  Design and implement tools for automated model tuning and hyperparameter optimization, as well as experiment analysis
·  Collaborate and communicate closely with researchers to identify, propose and build infrastructure, data and computation pipelines, data storage strategy, common libraries and useful tools needed to optimize research and development of deep-learning models
·  Research, validate and incorporate emerging machine learning and research infrastructures, tools, and technologies




About Uber

 

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 autonomous trucking and self-driving cars, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.
","responsibilities":"

About the Role

 

You will participate in the unique effort of bringing innovative state-of-the-art deep-learning models for self-driving into production, and onto autonomous vehicles. You will collaborate closely with a team of highly skilled researchers and engineers, tackling an array of challenges related to applying machine learning to self-driving vehicles. You will work on a variety of research engineering tasks related to algorithms for detection & perception, prediction, motion planning & automated map production, to name a few.
","qualifications":"

What You’ll Do

 

·  Develop in-depth understanding of deep learning models and algorithms and contribute to optimizing their training protocols, as well as test-time performance, runtime, memory footprint, and power consumption.
·  Design and implement tools for automated model tuning and hyperparameter optimization, as well as experiment analysis
·  Collaborate and communicate closely with researchers to identify, propose and build infrastructure, data and computation pipelines, data storage strategy, common libraries and useful tools needed to optimize research and development of deep-learning models
·  Research, validate and incorporate emerging machine learning and research infrastructures, tools, and technologies