As a 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 people who are as familiar with PyTorch as they are with signal phases, and for people who have turned neural nets into killer applications. 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.
- Developing applied solutions for real-world, complex problems in autonomous robotics and road transportation
- Designing for each stage in the ML model lifecycle, designing, training, testing, and deploying ML models.
- Applying data mining, ML, and other analysis techniques to solve complex business problems and working closely with the Product and Business Development teams to frame problems and build solutions that address real-world customer needs.
- Working with the Product and Engineering teams to ensure that the right data sets are being collected for relevant tasks at varying geospatial and temporal scales
- Participating in the engineering lifecycle and designing high-quality ML infrastructure and data pipelines, defining production code standards, conducting code reviews, and working alongside infrastructure, reliability, and hardware engineering teams.
- Expanding Cavnue’s competitive advantage through understanding, applying, or inventing novel ML and CS techniques
- MS or Ph.D. in Computer Science (or related field)
- 2-5 years of work experience, preferably in the autonomous vehicle, remote sensing, Smart City, or robotics space
- Deep expertise in statistics and scientific computing, familiarity with latest in productionized computer vision technologies and algorithms, particularly deep learning, reinforcement learning, classification, and pattern recognition.
- Preferred candidates will also have experience in statistical signal processing
- Ideal candidates will have experience leveraging techniques such as graph neural networks for developing predictive models for complex networks
- Firm grasp of large-scale data structures and data pipelines, data modeling, software architectures, and the latest ML libraries and frameworks e.g. TensorFlow, Pytorch.
- Python experience required. Jupyter experience ideal