Senior Machine Learning Engineer

We believe that the future of transportation is automated. Automated travel will be safer, more comfortable, more efficient and a powerful economic enabler for our communities. However, automating driving is a massively complex engineering challenge, requiring vehicles to navigate social norms, regional traffic patterns, unpredictable weather incidents, and a host of anomalous events. While billions of dollars have already been spent trying to solve this problem, a comprehensive answer remains frustratingly elusive. We believe that the final answer lies with roadway infrastructure.

Join us in building the roads of the future. Cavnue, which in April 2022 announced the closing of its Series A at $130M, is bridging technology and road infrastructure to realize a safer, more efficient, and more accessible future for automated transportation. Cavnue’s experienced team sits at the intersection of technology, infrastructure, and government—working together to develop and deploy the world’s most advanced roads. We are incorporating physical and digital infrastructure that unlock the full spectrum of capabilities of current and future automated vehicle technologies. We believe in a world in which road infrastructure shares in the complexity of autonomy and, instead of being another problem to solve for, becomes a core part of the solution.

The Role 

As a Senior Machine Learning Engineer at Cavnue, you’ll bring critical skills to a team working on changing the built environment to create safe, reliable, and secure mobility experiences. We’re looking for someone who has developed machine learning models into real applications and you can help us build and solve some of the most complex, near real-time coordination problems at scales that matter to everyday people using our streets.

Role overview: 

• Develop applied solutions for real-world, complex problems in autonomous robotics and road transportation
• Build real-world, production-scale AI capabilities to help solve practical but critical engineering challenges. You will work with machine learning frameworks as well as modern programming languages
• Design for each stage in the ML model lifecycle: development, training, evaluation, and deployment.
• Work with the Product and Systems Engineering teams to ensure that the right data sets are being collected for relevant tasks at varying geospatial and temporal scales
• Design, build and work with high-quality ML infrastructure and data pipelines, define production code standards, conduct code reviews, and work alongside infrastructure, reliability, and hardware engineering teams.
• Expand Cavnue’s competitive advantage by identifying, investigating, understanding and applying …