About the role
What you’ll be doing:
- Design and implement Machine Learning tooling and workflows for analysis & augmentation of real-world data
- Develop and optimize training pipelines in distributed environments
- Establish automated ETL pipelines
- Programmatically increase training efficiency of different neural network architectures
- Improve the developer experience and performance of our scalable ML platform
- Develop application/ML metrics to measure model, perception system, and overall self-driving performance; analyze for performance optimization opportunities
What you'll bring:
- BS or above in Computer Science/Engineering and 4+ years of industry experience in designing and implementing deep learning, ML, or data analytics infrastructure
- Experience working with production Machine Learning pipelines, from dataset collection and labeling to training and validation
- Knowledge in using common deep learning frameworks, e.g. Tensorflow, Pytorch, CaffeSkilled in C++ (11 or newer) and PythonDemonstrated experience in profiling CPU/GPU code
- Experience with developing, running, and managing container orchestration systems like Kubernetes
- Ability to thrive in a fast-moving, collaborative, small team environment with little supervision
What makes you a strong fit:
- Experience working with data processing pipelines for training in the cloud (AWS, Azure, Google Cloud, etc.)Solid understanding of metrics, data analysis, and scientific evaluation
- Strong Software Engineering skills building well-designed, highly-maintained and high-reliability code used by other engineers
- Passion for sustainable energy and electric vehicle development