Senior Principal Radar Autonomy Engineer

We are looking to add a talented Principal Radar Autonomy Engineer to join our Autonomy team. You will own developing state-of-the-art Radar systems, including hardware design, data processing, and developing machine learning models to ingest low-level radar data. You will work closely with a cross functional team to enable the entire sensor suite to empower end-to-end machine learning. As a hands-on engineering lead you will architect and co-develop both Radar sensors and machine learning pipelines to drive our next generation vehicles. Your deep applied research experience will inform your visionary approach to enable more intelligent, capable, and cost effective autonomous vehicles, and your deep engineering experience will enable pragmatic trade-off decisions. We are looking for someone that knows how to push the boundary of “the box”, and thrives working as the technical expert across both the hardware and software domains.  Does this sound like you?

What you'll be doing:

• Applied research and development of deep neural networks for end-to-end solutions.
• Own and deliver state-of-the-art Imaging Radar hardware and machine learning to push the performance boundaries for next generation Autonomous Vehicles.  
• Develop core deep learning codebase for efficient training and testing pipelines.
• Conduct deep learning experiments, write reports / publications, and file patents.
• Work cross functionally with high performance compute, systems, vehicle engineering, autonomy, and suppliers as the end-to-end Radar owner and domain expert to architect and define an optimal sub-system.
• Use your top-notch development expertise to inspire others to develop better practices and principles.
• Mentor junior researchers by providing guidance on research projects and design document reviews.

What we're looking for:

• Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, Electrical Engineering or a related field.
• Deep understanding of Radar hardware, Radar DSP, Radar machine learning, and Radar testing/performance that informs optimal machine learning performance.  
• Experience developing low-level data pipelines to enable industry leading machine learning performance. 
• Understanding of computer architecture principals and how data pipelines and machine learning algorithms map onto hardware. 
• In-depth understanding of common machine learning and deep learning algorithms (e.g. for classification, regression, and clustering).
• Experience designing, training, and analyzing neural networks for at least one of the following applications: object detection, image segmentation, sensor fusion, multitask learning, motion prediction, and/or tracking.
• Significant experience with software engineering principles including software design, source control management, build processes, code reviews, testing methods.
• Fluency in Python, including standard scientific computing libraries and Python bindings development experience.
• Experience with PyTorch or other deep learning frameworks.
• Experience defining data collection, data curation, and working with large data sets.
• Leadership and mentoring experience.

Bonus points (not required):

• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with physical simulation of Radar and other sensors.

Why you should join us:

• Join a world-class team of engineers including inventors of nuScenes (https://www.nuscenes.org), PointPillars (https://arxiv.org/abs/1812.05784), PointPainting (http://arxiv.org/abs/1911.10150), ADCNet (https://arxiv.org/pdf/2303.11420), and AAETR (https://arxiv.org/pdf/2408.09362).
• Lead an end-to-end Imaging Radar & machine learning development to set a new performance benchmark for what is possible.
• Design and implement the system the right way.
• Speaking and publication opportunities are encouraged and supported.

Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more.

Our journey is always people first.

We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.

Higher purpose, greater impact.

We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  

Scale up, not starting up.

Our team is 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 longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.

Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.

Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.