Senior DevOps Engineer, Machine Learning

Key Skills

Java Programming, Python Programming, C++, SQL, DevOps, AWS, Machine learning techniques, Jenkins, Deep Learning, Docker, Kubernetes-K8s, Continuous Integration & Continuous Delivery - CI/CD, Terraform, Linux Operating system, Bash scripting

Job Description

The Senior DevOps Engineer will be a member of and supporting our Machine Learning for Motion Planning software development and research team. This team introduces and advances new Deep Learning techniques for our Motion Planner. We are looking for an experienced DevOps engineer to help build and scale machine learning infrastructure, with the eventual goal of partnering with our research scientists to train models and write production level software.

Role responsibilities:

• Work with Python including Python package management, scripting, and reviewing code
• Create and deploy docker images dedicated for machine learning training
• Develop tools for deployment and monitoring of highly available, mission critical services
• Troubleshoot critical build and development issues
• Build and support cloud infrastructure including Kubernetes
• Establish and maintain a continuous integration infrastructure
• Manage/Upgrade the applications as necessary.

Impact:

• Work closely with robotics research and software engineers specialized in Machine Learning, Data, Motion Planning and Controls to identify places where data-driven deep learning approach is beneficial.

Experience to bring:

• DevOps
• Python, Bash scripting
• Cloud (AWS)
• Linux, version control, CI/CD (Jenkins)
• Terraform, Docker, Kubernetes

We'd be pleasantly surprised to see:

• Experience managing AWS and in-house server infrastructure using IaC
• Large-scale compute and build infrastructure.
• C++
• Bazel

Machine Learning at Motional:

We have produced groundbreaking advancements in the autonomous vehicle industry including

• nuScenes: large scale public data set for autonomous driving (https://www.nuscenes.org)
• PointPillars: Fast Encoders for Object Detection from Point Clouds (https://arxiv.org/abs/1812.05784)
• PointPainting: PointPainting: Sequential Fusion for 3D Object Detection (http://arxiv.org/abs/1911.10150) 
• nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles (https://arxiv.org/pdf/2106.11810.pdf)
• nuPlan (continued): (https://motional.com/news/nuplan)
• Predicting the future in real time for safer autonomous driving (https://motional.com/news/technically-speaking-predicting-the-future)
• Continuous Learning Framework (https://motional.com/news/technically-speaking-learning-with-every-mile-driven)
• Mining for scenarios to help better train our AVs (https://motional.com/news/technically-speaking-scenario-mining)

Company:

Motional is a driverless technology company making self-driving vehicles a safe, reliable, and accessible reality.

The Motional team is made up of engineers, researchers, innovators, dreamers and doers, who together are creating a first-of-its-kind technology with the potential to transform the way we move. We’re behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's most-established public robotaxi fleet.

As a leader in an emerging industry that has the promise of creating safer, more reliable and accessible transportation for all, it’s imperative we have a diverse workforce, create an inclusive culture, and understand the communities we operate in around the world.

Diversity, Equity and Inclusion is fundamental to Motional’s success. That’s why we’ve established a team to ensure DEI is at the forefront of the candidate and employee experience, beginning with the hiring process and continuing throughout your career. We also partner with organizations like the National Society of Black Engineers, the Society of Hispanic Professional Engineers, and the Society of Women Engineers to ensure our DEI work is informed by the latest thinking and approaches.