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Aurora

Aurora delivers the benefits of self-driving technology safely, quickly, and broadly.

Machine Learning Engineer - Motion Simulation
Pittsburgh, PA, US
Job Description / Skills Required

Bringing self-driving vehicles to our roads is the most transformative opportunity of our generation. Aurora is taking a fresh start with the development of self-driving technology, combining excellence in AI, rigorous engineering, and a team with decades of experience building robots that work.


Led by a team of seasoned experts, including three of the world’s leaders of self-driving technology, our mission is to deliver the benefits of self-driving technology safely, quickly, and broadly. We are designing the software and hardware to power the transportation of our future that will make our roads safer, give more people access to mobility, and reduce congestion and pollution in cities - improving the quality of life for all. The challenge in what we are endeavoring to achieve is transcendent; we are developing perhaps the world’s most complex computing system and asking it to perform the task of transporting and keeping safe our most precious asset: human life.


We’re looking for people who are as excited as we are to solve these complex problems and make this tremendous impact on our future, and who want to be surrounded by great people while we do it. We are searching for a Machine Learning Engineer for our Motion Simulation team. This individual will be designing and building out machine learning pipelines and models to learn about the behavior of vehicles and pedestrians from real world data, and then use that to generate realistic behavior of agents in simulations. 


Responsibilities


Develop cutting edge machine learning algorithms for simulating realistic interactions between autonomous vehicles and multiple actors in a virtual environment


Guide the development of these algorithms from prototype to production - including training on large scale datasets and deploying at scale in the cloud


Design models of behavior of virtual agents, and use machine learning to train these models from on-road observations


Work closely with motion planning and controls engineers to understand and document test requirements: then plan, develop and deliver simulation models to be used in millions of miles of virtual tests


Develop tools to simulate data from complex scenes and automate scenario creation


Essential


PhD in Machine Learning, Robotics, Computer Science, or related field, or equivalent practical experience


Excellent C++ programming and software design skills


Experience with Python


Strong machine learning skills, including supervised and unsupervised methods, generative and discriminative methods, and deep learning 


Experience applying Machine Learning to decision-making problems


Strong mathematical skills, including linear algebra, numerical methods, and stochastic methods


Passion for solving challenging, impactful problems 


Experience in applied machine learning, including data collection, analysis and feature engineering


Experience with machine learning frameworks such as Pytorch or Tensorflow


Desirable


Experience with Reinforcement Learning, Imitation Learning, Sequence Prediction or Recommendation Systems


Experience implementing diverse types of machine learning models (RNN, GAN, LSTM, etc.)


Experience building simulations for industry or research


Experience applying machine learning to problems in robotics


Experience with motion planning or behavior planning


Experience with large-scale cloud infrastructure, e.g. G-Cloud or AWS.


Experience bringing machine learning models to production


Working at Aurora


Our work has real purpose. Delivering self-driving will improve lives around the world, expanding access to transportation, revitalizing cities, giving people more time back every day.


We’re one team. We’re inspired by the challenge of what we’re solving and the impact our work will have on society. Our camaraderie is built on respect for our work and the fundamental belief our success will be a result of working together.


The Founding Team 


Aurora has assembled the most experienced leadership team in the space. Chris Urmson helped lead Carnegie Mellon’s efforts in Darpa’s Grand Challenges, then was a founding member of Google’s self-driving team. Sterling Anderson worked on the tech at MIT before leading Tesla’s Autopilot system. Drew Bagnell, also a Carnegie Mellon alum, is a machine learning expert who helped build Uber’s autonomy effort. At Aurora, these three continue to bring experts from all areas of the industry to the team. We are funded by some of Silicon Valley’s best venture capital firms, including Greylock and Index Venture